Paper Digest: Recent Papers on Relation Extraction
Paper Digest Team extracted all recent Relation Extraction related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic.
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TABLE 1: Paper Digest: Recent Papers on Relation Extraction
Paper | Author(s) | Source | Date | |
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1 | REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error propagation, and/or (ii) they are restricted to sentence level which prevents them from capturing long-range dependencies and results in expensive inference time. We address these limitations by proposing REXEL, a highly efficient and accurate model for the joint task of document level cIE (DocIE). |
Nacime Bouziani; Shubhi Tyagi; Joseph Fisher; Jens Lehmann; Andrea Pierleoni; | arxiv-cs.CL | 2024-04-19 |
2 | Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE task. To address this limitation, we propose the Variational Multi-Modal Hypergraph Attention Network (VM-HAN) for multi-modal relation extraction. |
QIAN LI et. al. | arxiv-cs.CL | 2024-04-18 |
3 | GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text. In this paper, we propose a novel approach to this task by formulating it as graph structure learning (GSL). |
Urchade Zaratiana; Nadi Tomeh; Niama El Khbir; Pierre Holat; Thierry Charnois; | arxiv-cs.CL | 2024-04-18 |
4 | EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, they disregard task and/or dataset-specific constraints, resulting in output structures that lack coherence. In our work, we introduce EnriCo, which mitigates these shortcomings. |
Urchade Zaratiana; Nadi Tomeh; Yann Dauxais; Pierre Holat; Thierry Charnois; | arxiv-cs.CL | 2024-04-18 |
5 | A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a model that, initialized from LayoutLMv3, can match or outperform the current state-of-the-art results in RE applied to Visually-Rich Documents (VRD) on FUNSD and CORD datasets, without any specific pre-training and with fewer parameters. |
WIAM ADNAN et. al. | arxiv-cs.CL | 2024-04-16 |
6 | Leveraging Data Augmentation for Process Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Research in the latter field is regularly faced with the problem of limited data availability, hindering both evaluation and development of new techniques, especially learning-based ones. To overcome this data scarcity issue, in this paper we investigate the application of data augmentation for natural language text data. |
Julian Neuberger; Leonie Doll; Benedict Engelmann; Lars Ackermann; Stefan Jablonski; | arxiv-cs.CL | 2024-04-11 |
7 | Causality Extraction from Nuclear Licensee Event Reports Using A Hybrid Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposed a hybrid framework for causality detection and extraction from nuclear licensee event reports. |
SOHAG RAHMAN et. al. | arxiv-cs.CL | 2024-04-08 |
8 | A Two Dimensional Feature Engineering Method for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a two-dimensional feature engineering method in the 2D sentence representation for relation extraction. |
Hao Wang; Yanping Chen; Weizhe Yang; Yongbin Qin; Ruizhang Huang; | arxiv-cs.CL | 2024-04-07 |
9 | SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: 118,075 human bounding boxes and 50,649 interaction instances are annotated on 11,398 keyframes. To benchmark this, we propose a two-stage baseline method and conduct extensive experiments to reveal the key factors for a successful human-human interaction detector. |
Tao Wu; Runyu He; Gangshan Wu; Limin Wang; | arxiv-cs.CV | 2024-04-06 |
10 | Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a meta dataset for few-shot relation extraction, which includes two datasets derived from existing supervised relation extraction datasets NYT29 (Takanobu et al., 2019; Nayak and Ng, 2020) and WIKIDATA (Sorokin and Gurevych, 2017) as well as a few-shot form of the TACRED dataset (Sabo et al., 2021). |
Fahmida Alam; Md Asiful Islam; Robert Vacareanu; Mihai Surdeanu; | arxiv-cs.CL | 2024-04-05 |
11 | Evaluating Generative Language Models in Information Extraction As Subjective Question Correction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. |
YUCHEN FAN et. al. | arxiv-cs.CL | 2024-04-04 |
12 | EGTR: Extracting Graph from Transformer for Scene Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Considering the dependency of the relation extraction task on the object detection task, we propose a novel relation smoothing technique that adjusts the relation label adaptively according to the quality of the detected objects. |
Jinbae Im; JeongYeon Nam; Nokyung Park; Hyungmin Lee; Seunghyun Park; | arxiv-cs.CV | 2024-04-02 |
13 | Efficient Information Extraction in Few-Shot Relation Classification Through Contrastive Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning. |
Philipp Borchert; Jochen De Weerdt; Marie-Francine Moens; | arxiv-cs.CL | 2024-03-25 |
14 | Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to A New Language Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There is a growing interest in the community to build datasets capable of training machine learning models to extract relationships. |
Alistair Plum; Tharindu Ranasinghe; Christoph Purschke; | arxiv-cs.CL | 2024-03-25 |
15 | Event Temporal Relation Extraction Based on Retrieval-Augmented on LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel retrieval-augmented TempRel extraction approach, leveraging knowledge retrieved from large language models (LLMs) to enhance prompt templates and verbalizers. |
Xiaobin Zhang; Liangjun Zang; Qianwen Liu; Shuchong Wei; Songlin Hu; | arxiv-cs.CL | 2024-03-22 |
16 | CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Centered around the outsourcing practices of companies within fund prospectuses in Germany, we introduce a dataset specifically designed for named entity recognition and relation extraction tasks. |
Neda Foroutan; Markus Schröder; Andreas Dengel; | arxiv-cs.CL | 2024-03-22 |
17 | MixRED: A Mix-lingual Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the lack of a dedicated dataset, the effectiveness of existing relation extraction models in such a scenario is largely unexplored. To address this issue, we introduce a novel task of considering relation extraction in the mix-lingual scenario called MixRE and constructing the human-annotated dataset MixRED to support this task. |
LINGXING KONG et. al. | arxiv-cs.AI | 2024-03-22 |
18 | CHisIEC: An Information Extraction Corpus for Ancient Chinese History Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our commitment to expediting ancient history and culture, we present the “Chinese Historical Information Extraction Corpus”(CHisIEC). |
Xuemei Tang; Zekun Deng; Qi Su; Hao Yang; Jun Wang; | arxiv-cs.CL | 2024-03-22 |
19 | AutoRE: Document-Level Relation Extraction with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, certain approaches resort to treating relations as candidate choices integrated into prompt templates, leading to inefficient processing and suboptimal performance when tackling Document-Level Relation Extraction (DocRE) tasks, which entail handling multiple relations and triplet facts distributed across a given document, posing distinct challenges. To overcome these limitations, we introduce AutoRE, an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts). |
Xue Lilong; Zhang Dan; Dong Yuxiao; Tang Jie; | arxiv-cs.CL | 2024-03-21 |
20 | GraphERE: Jointly Multiple Event-Event Relation Extraction Via Graph-Enhanced Event Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The interconnection between relations (e.g., temporal and causal relations usually interact with each other ) is ignored. To solve the above problems, this paper proposes a jointly multiple ERE framework called GraphERE based on Graph-enhanced Event Embeddings. |
Haochen Li; Di Geng; | arxiv-cs.CL | 2024-03-19 |
21 | Pipelined Biomedical Event Extraction Rivaling Joint Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional biomedical event extraction usually adopts a pipelined approach, which contains trigger identification, argument role recognition, and finally event construction either using specific rules or by machine learning. In this paper, we propose an n-ary relation extraction method based on the BERT pre-training model to construct Binding events, in order to capture the semantic information about an event’s context and its participants. |
Pengchao Wu; Xuefeng Li; Jinghang Gu; Longhua Qian; Guodong Zhou; | arxiv-cs.CL | 2024-03-18 |
22 | Information Extraction: An Application to The Domain of Hyper-local Financial Data on Developing Countries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the need for financial data on company activities in developing countries for development research and economic analysis, such data does not exist. In this project, we develop and evaluate two Natural Language Processing (NLP) based techniques to address this issue. |
Abuzar Royesh; Olamide Oladeji; | arxiv-cs.CL | 2024-03-13 |
23 | Advancing Biomedical Text Mining with Community Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, we review the recent advances in community challenges specific to Chinese biomedical text mining. |
HUI ZONG et. al. | arxiv-cs.AI | 2024-03-07 |
24 | Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. |
Robert Vacareanu; Fahmida Alam; Md Asiful Islam; Haris Riaz; Mihai Surdeanu; | arxiv-cs.CL | 2024-03-05 |
25 | DP-CRE: Continual Relation Extraction Via Decoupled Contrastive Learning and Memory Structure Preservation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To handle the problem, we introduce the DecouPled CRE (DP-CRE) framework that decouples the process of prior information preservation and new knowledge acquisition. |
Mengyi Huang; Meng Xiao; Ludi Wang; Yi Du; | arxiv-cs.CL | 2024-03-05 |
26 | AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Objectives: Our objective is to create an end-to-end system called AutoRD, which automates extracting information from clinical text about rare diseases. |
Lang Cao; Jimeng Sun; Adam Cross; | arxiv-cs.CL | 2024-03-01 |
27 | Few-Shot Relation Extraction with Hybrid Visual Evidence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a multi-modal few-shot relation extraction model (MFS-HVE) that leverages both textual and visual semantic information to learn a multi-modal representation jointly. |
Jiaying Gong; Hoda Eldardiry; | arxiv-cs.CL | 2024-03-01 |
28 | Pointing Out The Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we describe several semantically-motivated strategies to generate adversarial examples by replacing entity mentions and investigate how state-of-the-art RE models perform under pressure. |
Gennaro Nolano; Moritz Blum; Basil Ell; Philipp Cimiano; | arxiv-cs.CL | 2024-02-29 |
29 | On The Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new framework, Clean-LaVe, which aims to utilize silver standard data to enhance the zero-shot performance. |
Jianwei Wang; Tianyin Wang; Ziqian Zeng; | arxiv-cs.CL | 2024-02-28 |
30 | Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper harnesses prompt learning to explore the implicit capabilities of pre-trained language models to address the above two challenges, thereby making language models better continual few-shot relation extractors. Specifically, we propose a Contrastive Prompt Learning framework, which designs prompt representation to acquire more generalized knowledge that can be easily adapted to old and new categories, and margin-based contrastive learning to focus more on hard samples, therefore alleviating catastrophic forgetting and overfitting issues. |
Shengkun Ma; Jiale Han; Yi Liang; Bo Cheng; | arxiv-cs.CL | 2024-02-23 |
31 | Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents our effort in the data acquisition, annotation methodology, and thorough analysis of the annotated dataset. |
Mohan Raj Chanthran; Lay-Ki Soon; Huey Fang Ong; Bhawani Selvaretnam; | arxiv-cs.CL | 2024-02-22 |
32 | Small Language Model Is A Good Guide for Large Language Model in Chinese Entity Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: An important problem in the field of RE is long-tailed data, while not much attention is currently paid to this problem using LLM approaches. Therefore, in this paper, we propose SLCoLM, a model collaboration framework, to mitigate the data long-tail problem. |
Xuemei Tang; Jun Wang; Qi Su; | arxiv-cs.CL | 2024-02-22 |
33 | Combining Language and Graph Models for Semi-structured Information Extraction on The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present GraphScholarBERT, an open-domain information extraction method based on a joint graph and language model structure. |
Zhi Hong; Kyle Chard; Ian Foster; | arxiv-cs.IR | 2024-02-21 |
34 | Zero-shot Generalization Across Architectures for Visual Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Using a minimalist vision dataset and a measure of generalizability, we show that popular networks, from deep convolutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and across architectures. |
Evan Gerritz; Luciano Dyballa; Steven W. Zucker; | arxiv-cs.CV | 2024-02-21 |
35 | How Important Is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Cutting edge techniques developed in the general NLP domain are often subsequently applied to the high-value, data-rich biomedical domain. |
Aviv Brokman; Ramakanth Kavuluru; | arxiv-cs.CL | 2024-02-20 |
36 | Creating A Fine Grained Entity Type Taxonomy Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigate the potential of GPT-4 and its advanced iteration, GPT-4 Turbo, in autonomously developing a detailed entity type taxonomy. |
Michael Gunn; Dohyun Park; Nidhish Kamath; | arxiv-cs.CL | 2024-02-19 |
37 | GenRES: Rethinking Evaluation for Generative Relation Extraction in The Era of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This shortfall arises because these metrics rely on exact matching with human-annotated reference relations, while GRE methods often produce diverse and semantically accurate relations that differ from the references. To fill this gap, we introduce GenRES for a multi-dimensional assessment in terms of the topic similarity, uniqueness, granularity, factualness, and completeness of the GRE results. |
Pengcheng Jiang; Jiacheng Lin; Zifeng Wang; Jimeng Sun; Jiawei Han; | arxiv-cs.CL | 2024-02-16 |
38 | Grasping The Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the strong synthetic data generation power of LLMs, we propose a framework REPaL which consists of three stages: (1) We utilize LLMs to generate initial seed instances based on relation definitions and an unlabeled corpora. |
Sizhe Zhou; Yu Meng; Bowen Jin; Jiawei Han; | arxiv-cs.CL | 2024-02-16 |
39 | Prompting Implicit Discourse Relation Annotation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work investigates several proven prompting techniques to improve ChatGPT’s recognition of discourse relations. |
Frances Yung; Mansoor Ahmad; Merel Scholman; Vera Demberg; | arxiv-cs.CL | 2024-02-07 |
40 | Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We evaluate our approach on three benchmark datasets: DocRED, ReDocRED, and DWIE. |
Monika Jain; Raghava Mutharaju; Ramakanth Kavuluru; Kuldeep Singh; | arxiv-cs.IR | 2024-01-22 |
41 | Distantly Supervised Morpho-Syntactic Model for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a method for the extraction and categorisation of an unrestricted set of relationships from text. |
Nicolas Gutehrlé; Iana Atanassova; | arxiv-cs.CL | 2024-01-18 |
42 | MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder-decoder framework, to jointly extract entities and relations from material science articles as a triplet ($entity1, relation, entity2$). |
ANKAN MULLICK et. al. | arxiv-cs.CL | 2024-01-18 |
43 | BERTologyNavigator: Advanced Question Answering with BERT-based Semantics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce the BERTologyNavigator — a two-phased system that combines relation extraction techniques and BERT embeddings to navigate the relationships within the DBLP Knowledge Graph (KG). |
Shreya Rajpal; Ricardo Usbeck; | arxiv-cs.CL | 2024-01-17 |
44 | Dynamic Relation Transformer for Contextual Text Block Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new framework that frames CTBD as a graph generation problem. |
JIAWEI WANG et. al. | arxiv-cs.CV | 2024-01-17 |
45 | EMBRE: Entity-aware Masking for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Entity-aware Masking for Biomedical Relation Extraction (EMBRE) method for biomedical relation extraction, as applied in the context of the BioRED challenge Task 1, in which human-annotated entities are provided as input. |
Mingjie Li; Karin Verspoor; | arxiv-cs.CL | 2024-01-15 |
46 | Joint Extraction of Uyghur Medicine Knowledge with Edge Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This mode faces challenges such as error propagation between tasks, insufficient consideration of dependencies between the two subtasks, and the neglect of interrelations between different relations within a sentence. To address these challenges, a joint extraction model with parameter sharing in edge computing is proposed, named CoEx-Bert. |
Fan Lu; Quan Qi; Huaibin Qin; | arxiv-cs.CL | 2024-01-13 |
47 | Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. |
Jiaxin Wang; Lingling Zhang; Jun Liu; Tianlin Guo; Wenjun Wu; | arxiv-cs.CL | 2024-01-11 |
48 | PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, simply concatenating SER and RE serially can lead to severe error propagation, and it fails to handle cases like multi-line entities in real scenarios. To address these issues, this paper introduces a novel framework, PEneo (Pair Extraction new decoder option), which performs document pair extraction in a unified pipeline, incorporating three concurrent sub-tasks: line extraction, line grouping, and entity linking. |
ZENING LIN et. al. | arxiv-cs.CL | 2024-01-07 |
49 | An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel method for joint entity and relation extraction from unstructured text by framing it as a conditional sequence generation problem. |
Urchade Zaratiana; Nadi Tomeh; Pierre Holat; Thierry Charnois; | arxiv-cs.CL | 2024-01-02 |
50 | Improving Low-resource Prompt-based Relation Representation with Multi-view Decoupling Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we highlight the importance of learning high-quality relation representation in low-resource scenarios for RE, and propose a novel prompt-based relation representation method, named MVRE (\underline{M}ulti-\underline{V}iew \underline{R}elation \underline{E}xtraction), to better leverage the capacity of PLMs to improve the performance of RE within the low-resource prompt-tuning paradigm. |
CHENGHAO FAN et. al. | arxiv-cs.CL | 2023-12-26 |
51 | Open-Vocabulary Video Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, many current video understanding tasks prioritize general action classification and overlook the actors and relationships that shape the nature of the action, resulting in a superficial understanding of the action. Motivated by this, we introduce Open-vocabulary Video Relation Extraction (OVRE), a novel task that views action understanding through the lens of action-centric relation triplets. |
Wentao Tian; Zheng Wang; Yuqian Fu; Jingjing Chen; Lechao Cheng; | arxiv-cs.CV | 2023-12-25 |
52 | EDeR: Towards Understanding Dependency Relations Between Events Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such relationships consider two events to be independent in terms of syntax and semantics, but they fail to recognize the interdependence between events. To bridge this gap, we introduce a human-annotated Event Dependency Relation dataset (EDeR). |
Ruiqi Li; Patrik Haslum; Leyang Cui; | emnlp | 2023-12-22 |
53 | GPT-RE: In-context Learning for Relation Extraction Using Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose GPT-RE to successfully address the aforementioned issues by (1) incorporating task-aware representations in demonstration retrieval; and (2) enriching the demonstrations with gold label-induced reasoning logic. |
ZHEN WAN et. al. | emnlp | 2023-12-22 |
54 | Anaphor Assisted Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance. To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks. |
CHONGGANG LU et. al. | emnlp | 2023-12-22 |
55 | RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. |
SHIAO MENG et. al. | emnlp | 2023-12-22 |
56 | Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose HyperGraph neural network for ERE (HGERE), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). |
Zhaohui Yan; Songlin Yang; Wei Liu; Kewei Tu; | emnlp | 2023-12-22 |
57 | Open-world Semi-supervised Generalized Relation Discovery Aligned in A Real-world Setting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, we observe that popular relations can often be implicitly inferred through specific patterns, while long-tail relations tend to be explicitly expressed. Motivated by these insights, we present a method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data. |
William Hogan; Jiacheng Li; Jingbo Shang; | emnlp | 2023-12-22 |
58 | TIMELINE: Exhaustive Annotation of Temporal Relations Supporting The Automatic Ordering of Events in News Articles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Temporal relation extraction models have thus far been hindered by a number of issues in existing temporal relation-annotated news datasets, including: (1) low inter-annotator agreement due to the lack of specificity of their annotation guidelines in terms of what counts as a temporal relation; (2) the exclusion of long-distance relations within a given document (those spanning across different paragraphs); and (3) the exclusion of events that are not centred on verbs. This paper aims to alleviate these issues by presenting a new annotation scheme that clearly defines the criteria based on which temporal relations should be annotated. |
Sarah Alsayyahi; Riza Batista-Navarro; | emnlp | 2023-12-22 |
59 | CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. |
Philipp Borchert; Jochen De Weerdt; Kristof Coussement; Arno De Caigny; Marie-Francine Moens; | emnlp | 2023-12-22 |
60 | KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the flexibility of language expression and the lack of high-quality Chinese annotation datasets, it is still a challenge to accurately identify such relations from Chinese unstructured texts. To tackle this problem, we propose a Knowledge Enhanced Prompt Learning (KEPL) method for Chinese hypernym-hyponym relation extraction. |
Ningchen Ma; Dong Wang; Hongyun Bao; Lei He; Suncong Zheng; | emnlp | 2023-12-22 |
61 | Document-level Relationship Extraction By Bidirectional Constraints of Beta Rules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we proposes Bidirectional Constraints of Beta Rules(BCBR), a novel logic constraint framework. |
YICHUN LIU et. al. | emnlp | 2023-12-22 |
62 | HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By investigating the class separation of an FSRE model, we find that model upper layers are prone to learn relation-specific knowledge. Therefore, in this paper, we propose a HyperNetwork-based Decoupling approach to improve the generalization of FSRE models. |
LIANG ZHANG et. al. | emnlp | 2023-12-22 |
63 | Improving Unsupervised Relation Extraction By Augmenting Diverse Sentence Pairs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose AugURE with both within-sentence pairs augmentation and augmentation through cross-sentence pairs extraction to increase the diversity of positive pairs and strengthen the discriminative power of contrastive learning. |
Qing Wang; Kang Zhou; Qiao Qiao; Yuepei Li; Qi Li; | emnlp | 2023-12-22 |
64 | Generating Commonsense Counterfactuals for Stable Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, to identify causal terms accurately, we introduce an intervention-based strategy and leverage a constituency parser for correction. |
Xin Miao; Yongqi Li; Tieyun Qian; | emnlp | 2023-12-22 |
65 | Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, vanilla in-context learning is infeasible for DocRE due to the plenty of predefined fine-grained relation types and the uncontrolled generations of LLMs. To tackle this issue, we propose a method integrating an LLM and a natural language inference (NLI) module to generate relation triples, thereby augmenting document-level relation datasets. |
Junpeng Li; Zixia Jia; Zilong Zheng; | emnlp | 2023-12-22 |
66 | Rationale-Enhanced Language Models Are Better Continual Relation Learners Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the issue, we introduce rationale, i. e. , the explanations of relation classification results generated by Large Language Models (LLM), into CRE task. |
Weimin Xiong; Yifan Song; Peiyi Wang; Sujian Li; | emnlp | 2023-12-22 |
67 | Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, how to effectively harness massive instance-label pairs to encompass the learned representation with semantic richness in this learning paradigm is not fully explored. To address this gap, we introduce a novel synergistic anchored contrastive pre-training framework. |
DA LUO et. al. | arxiv-cs.CL | 2023-12-19 |
68 | HyperPIE: Hyperparameter Information Extraction from Scientific Publications Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we formalize and tackle hyperparameter information extraction (HyperPIE) as an entity recognition and relation extraction task. |
Tarek Saier; Mayumi Ohta; Takuto Asakura; Michael Färber; | arxiv-cs.CL | 2023-12-17 |
69 | MORE: A Multimodal Object-Entity Relation Extraction Dataset with A Benchmark Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new task, namely Multimodal Object-Entity Relation Extraction, which aims to extract object-entity relational facts from image and text data. |
LIANG HE et. al. | arxiv-cs.MM | 2023-12-15 |
70 | High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered By Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Methods: We formulate the relation extraction task as binary classifications for large language models. |
Songchi Zhou; Sheng Yu; | arxiv-cs.CL | 2023-12-13 |
71 | Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the observation, we propose a frustratingly easy method called SEQ* for IL with PLMs. |
Junhao Zheng; Shengjie Qiu; Qianli Ma; | arxiv-cs.CL | 2023-12-12 |
72 | BED: Bi-Encoder-Decoder Model for Canonical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, they are incapable of representing novel entities, since no embeddings have been learned for them. In this paper, we propose a novel framework, Bi-Encoder-Decoder (BED), to solve the above issues. |
Nantao Zheng; Siyu Long; Xinyu Dai; | arxiv-cs.CL | 2023-12-12 |
73 | Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. Methods We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion parameters. |
CHENG PENG et. al. | arxiv-cs.CL | 2023-12-10 |
74 | Comparison of Pipeline, Sequence-to-sequence, and GPT Models for End-to-end Relation Extraction: Experiments with The Rare Disease Use-case Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to compare three prevailing paradigms for E2ERE using a complex dataset focused on rare diseases involving discontinuous and nested entities. |
Shashank Gupta; Xuguang Ai; Ramakanth Kavuluru; | arxiv-cs.CL | 2023-11-22 |
75 | How Well ChatGPT Understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we assess ChatGPT’s capability in extracting entities and relations from the Malaysian English News (MEN) dataset. |
Mohan Raj Chanthran; Lay-Ki Soon; Huey Fang Ong; Bhawani Selvaretnam; | arxiv-cs.CL | 2023-11-20 |
76 | Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical NLP tasks in different languages, We present Taiyi, a bilingual fine-tuned LLM for diverse biomedical tasks. |
LING LUO et. al. | arxiv-cs.CL | 2023-11-20 |
77 | MAVEN-Arg: Completing The Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. |
XIAOZHI WANG et. al. | arxiv-cs.CL | 2023-11-15 |
78 | GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The advent of Large Language Models (LLMs) heralds a paradigm shift, suggesting the feasibility of a singular model addressing multiple IE subtasks. In this vein, we introduce the General Information Extraction Large Language Model (GIELLM), which integrates text Classification, Sentiment Analysis, Named Entity Recognition, Relation Extraction, and Event Extraction using a uniform input-output schema. |
Chengguang Gan; Qinghao Zhang; Tatsunori Mori; | arxiv-cs.CL | 2023-11-12 |
79 | Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach for few-shot relation extraction using large language models, named CoT-ER, chain-of-thought with explicit evidence reasoning. |
Xilai Ma; Jing Li; Min Zhang; | arxiv-cs.CL | 2023-11-10 |
80 | Relation Extraction in Underexplored Biomedical Domains: A Diversity-optimised Sampling and Synthetic Data Generation Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We provide our best performing (f1-score=59.0) BioGPT-Large model for end-to-end RE of natural-products relationships along with all the generated synthetic data and the evaluation dataset. |
Maxime Delmas; Magdalena Wysocka; André Freitas; | arxiv-cs.CL | 2023-11-10 |
81 | Improving Vision-and-Language Reasoning Via Spatial Relations Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most importantly, the spatial distribution of the visual objects is basically neglected. To address the above issue, we propose to construct the spatial relation graph based on the given visual scenario. |
CHENG YANG et. al. | arxiv-cs.CV | 2023-11-09 |
82 | Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning. |
YICHAO CAO et. al. | arxiv-cs.CV | 2023-11-07 |
83 | Relation Extraction Model Based on Semantic Enhancement Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the contribution of the possible subject, finally combine the relationship pre-classification results to weight the enhanced semantics of each relationship to find the enhanced semantics of the possible subject, and send the enhanced semantics combined with the possible subject to the object and relationship extraction module. |
Peiyu Liu; Junping Du; Yingxia Shao; Zeli Guan; | arxiv-cs.CL | 2023-11-05 |
84 | Let’s Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus and affected by the characteristics of the input text.To address these limitations, we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural knowledge base for API relation inference. |
QING HUANG et. al. | arxiv-cs.SE | 2023-11-02 |
85 | Discourse Relations Classification and Cross-Framework Discourse Relation Classification Through The Lens of Cognitive Dimensions: An Empirical Investigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that discourse relations can be effectively captured by some simple cognitively inspired dimensions proposed by Sanders et al.(2018). |
Yingxue Fu; | arxiv-cs.CL | 2023-11-01 |
86 | Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. |
Fei Cheng; Masayuki Asahara; Ichiro Kobayashi; Sadao Kurohashi; | arxiv-cs.CL | 2023-10-31 |
87 | Relation-driven Query of Multiple Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We proposed RelaQ, an interactive system that supports the time series query via relation specifications. |
SHUHAN LIU et. al. | arxiv-cs.HC | 2023-10-30 |
88 | A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance. |
Sakher Khalil Alqaaidi; Elika Bozorgi; Afsaneh Shams; Krzysztof Kochut; | arxiv-cs.CL | 2023-10-29 |
89 | Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data. |
HAO ZHANG et. al. | arxiv-cs.LG | 2023-10-29 |
90 | Nearest Neighbor Search Over Vectorized Lexico-Syntactic Patterns for Relation Extraction from Financial Documents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Apart from this, Real world financial documents such as various 10-X reports (including 10-K, 10-Q, etc.) of publicly traded companies pose another challenge to rule-based systems in terms of longer and complex sentences. In this paper, we introduce a simple approach that consults training relations at test time through a nearest-neighbor search over dense vectors of lexico-syntactic patterns and provides a simple yet effective means to tackle the above issues. |
Pawan Kumar Rajpoot; Ankur Parikh; | arxiv-cs.CL | 2023-10-26 |
91 | Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a deep learning model tailored for document information analysis, emphasizing document classification, entity relation extraction, and document visual question answering. |
Tofik Ali; Partha Pratim Roy; | arxiv-cs.CV | 2023-10-25 |
92 | Prompt Me Up: Unleashing The Power of Alignments for Multimodal Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite attempts at various fusions, previous works have overlooked many unlabeled image-caption pairs, such as NewsCLIPing. This paper proposes innovative pre-training objectives for entity-object and relation-image alignment, extracting objects from images and aligning them with entity and relation prompts for soft pseudo-labels. |
XUMING HU et. al. | arxiv-cs.CL | 2023-10-25 |
93 | Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning. |
YICHAO CAO et. al. | nips | 2023-10-24 |
94 | RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. |
SHIAO MENG et. al. | arxiv-cs.CL | 2023-10-24 |
95 | Document-Level In-Context Few-Shot Relation Extraction Via Pre-Trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a remedy, we present a novel framework for document-level in-context few-shot relation extraction via pre-trained language models. |
Yilmazcan Ozyurt; Stefan Feuerriegel; Ce Zhang; | arxiv-cs.CL | 2023-10-17 |
96 | Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It briefly discusses the main approaches and the pros and cons of each method. |
Serafina Kamp; Morteza Fayazi; Zineb Benameur-El; Shuyan Yu; Ronald Dreslinski; | arxiv-cs.IR | 2023-10-17 |
97 | Rethinking Relation Classification with Graph Meaning Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the growing interest, a critical gap persists in understanding the exact influence of GMRs, particularly concerning relation extraction tasks. Addressing this, we introduce DAGNN-plus, a simple and parameter-efficient neural architecture designed to decouple contextual representation learning from structural information propagation. |
Li Zhou; Wenyu Chen; Dingyi Zeng; Malu Zhang; Daniel Hershcovich; | arxiv-cs.CL | 2023-10-15 |
98 | PromptRE: Weakly-Supervised Document-Level Relation Extraction Via Prompting-Based Data Programming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Weakly-supervised document-level relation extraction faces significant challenges due to an imbalanced number no relation instances and the failure of directly probing pretrained large language models for document relation extraction. To address these challenges, we propose PromptRE, a novel weakly-supervised document-level relation extraction method that combines prompting-based techniques with data programming. |
Chufan Gao; Xulin Fan; Jimeng Sun; Xuan Wang; | arxiv-cs.CL | 2023-10-13 |
99 | Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Methods We developed a soft prompt-based LLM model and compared 4 training strategies including (1) fine-tuning without prompts; (2) hard-prompt with unfrozen LLMs; (3) soft-prompt with unfrozen LLMs; and (4) soft-prompt with frozen LLMs. |
CHENG PENG et. al. | arxiv-cs.CL | 2023-10-09 |
100 | Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary Relational Knowledge Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the construction of NKGs still significantly relies on manual labor, and n-ary relation extraction still remains at a course-grained level, which is always in a single schema and fixed arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. |
HAORAN LUO et. al. | arxiv-cs.AI | 2023-10-08 |
101 | Revisiting Large Language Models As Zero-shot Relation Extractors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the summarize-and-ask (\textsc{SumAsk}) prompting, a simple prompt recursively using LLMs to transform RE inputs to the effective question answering (QA) format. |
Guozheng Li; Peng Wang; Wenjun Ke; | arxiv-cs.AI | 2023-10-08 |
102 | Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing research primarily addresses only one type of noise, thereby limiting the effectiveness of noise reduction. To fill this gap, we introduce a new noise-robust approach, that 1)~incorporates a pre-trained GPT-2 into a sequence tagging scheme for simultaneous entity and relation detection, and 2)~employs a noise-robust learning framework which includes a new loss function that penalizes inconsistency with both significant relation patterns and entity-relation dependencies, as well as a self-adaptive learning step that iteratively selects and trains on high-quality instances. |
YUFEI LI et. al. | arxiv-cs.CL | 2023-10-07 |
103 | Extraction of Medication and Temporal Relation from Clinical Text Using Neural Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Clinical texts, represented in electronic medical records (EMRs), contain rich medical information and are essential for disease prediction, personalised information … |
Hangyu Tu; Lifeng Han; Goran Nenadic; | arxiv-cs.CL | 2023-10-03 |
104 | Do The Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we benchmark state-of-the-art pipeline and joint extraction models on sentence-level as well as document-level datasets. |
Pratik Saini; Tapas Nayak; Indrajit Bhattacharya; | arxiv-cs.CL | 2023-10-01 |
105 | Siamese Representation Learning for Unsupervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, fine-grained relational semantic in relationship makes spurious negative samples, damaging the inherent hierarchical structure and hindering performances. To tackle this problem, we propose Siamese Representation Learning for Unsupervised Relation Extraction — a novel framework to simply leverage positive pairs to representation learning, possessing the capability to effectively optimize relation representation of instances and retain hierarchical information in relational feature space. |
Guangxin Zhang; Shu Chen; | arxiv-cs.CL | 2023-09-30 |
106 | A Comprehensive Survey of Document-level Relation Extraction (2016-2023) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This task has gained increased interest as a viable solution to build and populate knowledge bases automatically from unstructured large-scale documents (e.g., scientific papers, legal contracts, or news articles), in order to have a better understanding of relationships between entities. This paper aims to provide a comprehensive overview of recent advances in this field, highlighting its different applications in comparison to sentence-level relation extraction. |
JULIEN DELAUNAY et. al. | arxiv-cs.CL | 2023-09-28 |
107 | OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To facilitate related research and application, we present an event understanding toolkit OmniEvent, which features three desiderata: (1) Comprehensive. |
HAO PENG et. al. | arxiv-cs.CL | 2023-09-25 |
108 | PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we approach the problem from a calibration perspective and propose PRiSM, which learns to adapt logits based on relation semantic information. |
Minseok Choi; Hyesu Lim; Jaegul Choo; | arxiv-cs.CL | 2023-09-25 |
109 | Multiple Relations Classification Using Imbalanced Predictions Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The pattern arises from the presence of a few valid relations that need positive labeling in a relatively large predefined relations set. We propose a multiple relations classification model that tackles these issues through a customized output architecture and by exploiting additional input features. |
Sakher Khalil Alqaaidi; Elika Bozorgi; Krzysztof J. Kochut; | arxiv-cs.CL | 2023-09-24 |
110 | ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. |
ZHILEI HU et. al. | arxiv-cs.CL | 2023-09-22 |
111 | BitCoin: Bidirectional Tagging and Supervised Contrastive Learning Based Joint Relational Triple Extraction Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They solely focus on extracting relational triples from subject to object, neglecting that once the extraction of a subject fails, it fails in extracting all triples associated with that subject. To address these issues, we propose BitCoin, an innovative Bidirectional tagging and supervised Contrastive learning based joint relational triple extraction framework. |
Luyao He; Zhongbao Zhang; Sen Su; Yuxin Chen; | arxiv-cs.CL | 2023-09-21 |
112 | Dealing with Negative Samples with Multi-task Learning on Span-based Joint Entity-relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models encounter a significant number of non-entity spans or irrelevant span pairs during the tasks, impairing model performance significantly. To address this issue, this paper introduces a span-based multitask entity-relation joint extraction model. |
Chenguang Xue; Jiamin Lu; | arxiv-cs.CL | 2023-09-18 |
113 | Comparative Analysis of Contextual Relation Extraction Based on Deep Learning Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, deep learning techniques have been used to identify the appropriate semantic relation based on the context from multiple sentences. |
R. Priyadharshini; G. Jeyakodi; P. Shanthi Bala; | arxiv-cs.CL | 2023-09-13 |
114 | Zero-shot Information Extraction from Radiological Reports Using ChatGPT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we aim to explore whether the most popular large language model, ChatGPT, can extract useful information from the radiological reports. |
Danqing Hu; Bing Liu; Xiaofeng Zhu; Xudong Lu; Nan Wu; | arxiv-cs.CL | 2023-09-04 |
115 | GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current methods rely on text-based encoders and employ various hand-coded pooling heuristics to aggregate information from entity mentions and associated contexts. In this paper, we replace these rigid pooling functions with explicit graph relations by leveraging the intrinsic graph processing capabilities of the Transformer model. |
Andrei C. Coman; Christos Theodoropoulos; Marie-Francine Moens; James Henderson; | arxiv-cs.CL | 2023-08-28 |
116 | SCoRD: Subject-Conditional Relation Detection with Text-Augmented Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the Open Images dataset, we propose a challenging OIv6-SCoRD benchmark such that the training and testing splits have a distribution shift in terms of the occurrence statistics of $\langle$subject, relation, object$\rangle$ triplets. To solve this problem, we propose an auto-regressive model that given a subject, it predicts its relations, objects, and object locations by casting this output as a sequence of tokens. |
ZIYAN YANG et. al. | arxiv-cs.CV | 2023-08-24 |
117 | Exploring Effective Inter-Encoder Semantic Interaction for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a document-level RE model with a Graph-Transformer Network (GTN). |
LIANG ZHANG et. al. | ijcai | 2023-08-23 |
118 | Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network for Spatial-Temporal Action Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network (AMCRNet) that extracts multi-scale context through multiple pooling layers with different sizes. |
JUN YU et. al. | ijcai | 2023-08-23 |
119 | RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including two novel tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). |
YANG BAI et. al. | ijcai | 2023-08-23 |
120 | CARE: Co-Attention Network for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing joint extraction methods suffer from issues of feature confusion or inadequate interaction between the two subtasks. Addressing these challenges, in this work, we propose a Co-Attention network for joint entity and Relation Extraction (CARE). |
Wenjun Kong; Yamei Xia; | arxiv-cs.CL | 2023-08-23 |
121 | PasCore: A Chinese Overlapping Relation Extraction Model Based on Global Pointer Annotation Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Especially, overlapping entities are common and indispensable in Chinese. To address this issue, this paper proposes PasCore, which utilizes a global pointer annotation strategy for overlapping relation extraction in Chinese. |
Peng Wang; Jiafeng Xie; Xiye Chen; Guozheng Li; Wei Li; | ijcai | 2023-08-23 |
122 | Extracting Relational Triples Based on Graph Recursive Neural Network Via Dynamic Feedback Forest Algorithm Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel approach that converts the triple extraction task into a graph labeling problem, capitalizing on the structural information of dependency parsing and graph recursive neural networks (GRNNs). |
Hongyin Zhu; | arxiv-cs.CL | 2023-08-22 |
123 | Synthesizing Political Zero-Shot Relation Classification Via Codebook Knowledge, NLI, and ChatGPT Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our study investigates zero-shot learning methods that utilize only expert knowledge from existing annotation codebook. |
YIBO HU et. al. | arxiv-cs.CL | 2023-08-15 |
124 | Action Class Relation Detection and Classification Across Multiple Video Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a unified model to predict relations between action classes, using language and visual information associated with classes. |
Yuya Yoshikawa; Yutaro Shigeto; Masashi Shimbo; Akikazu Takeuchi; | arxiv-cs.CV | 2023-08-14 |
125 | RadGraph2: Modeling Disease Progression in Radiology Reports Via Hierarchical Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present RadGraph2, a novel dataset for extracting information from radiology reports that focuses on capturing changes in disease state and device placement over time. |
SAMEER KHANNA et. al. | arxiv-cs.CL | 2023-08-09 |
126 | DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work introduces a new benchmark dataset DialogRE^C+, introducing coreference resolution into the DRE scenario. |
YIYUN XIONG et. al. | arxiv-cs.CL | 2023-08-08 |
127 | Ahead of The Text: Leveraging Entity Preposition for Financial Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result of our methodology, we achieved the 1st place ranking on the competition’s public leaderboard. |
Stefan Pasch; Dimitrios Petridis; | arxiv-cs.CL | 2023-08-08 |
128 | Explaining Relation Classification Models with Semantic Extents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. |
Lars Klöser; Andre Büsgen; Philipp Kohl; Bodo Kraft; Albert Zündorf; | arxiv-cs.CL | 2023-08-04 |
129 | FinTree: Financial Dataset Pretrain Transformer Encoder for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present FinTree, Financial Dataset Pretrain Transformer Encoder for Relation Extraction. |
Hyunjong Ok; | arxiv-cs.CL | 2023-07-25 |
130 | Reducing Spurious Correlations for Relation Extraction By Feature Decomposition and Semantic Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a spurious correlation reduction method for RE via feature decomposition and semantic augmentation (denoted as FDSA). |
Tianshu Yu; Min Yang; Chengming Li; Ruifeng Xu; | sigir | 2023-07-25 |
131 | SelfLRE: Self-refining Representation Learning for Low-resource Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we propose a novel LRE architecture named SelfLRE, which leverages two complementary modules, one module uses self-training to obtain pseudo-labels for unlabeled data, and the other module uses self-ensembling learning to obtain the task-agnostic representations, and leverages the existing pseudo-labels to refine the better task-specific representations on unlabeled data. |
Xuming Hu; Junzhe Chen; Shiao Meng; Lijie Wen; Philip S. Yu; | sigir | 2023-07-25 |
132 | Think Rationally About What You See: Continuous Rationale Extraction for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. |
Xuming Hu; Zhaochen Hong; Chenwei Zhang; Irwin King; Philip Yu; | sigir | 2023-07-25 |
133 | REFinD: Relation Extraction Financial Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with ~29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. |
SIMERJOT KAUR et. al. | sigir | 2023-07-25 |
134 | Personalized Federated Relation Classification Over Heterogeneous Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to investigate personalized federated relation classification, in which strong client models adapted to their own data are desired. |
Ning Pang; Xiang Zhao; Weixin Zeng; Ji Wang; Weidong Xiao; | sigir | 2023-07-25 |
135 | Exploiting Ubiquitous Mentions for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address it, we propose to incorporate coreferences (e.g. pronouns and common nouns) into mentions, based on which we refine and re-annotate the widely-used DocRED benchmark as R-DocRED. |
Ruoyu Zhang; Yanzeng Li; Minhao Zhang; Lei Zou; | sigir | 2023-07-25 |
136 | In Defense of Clip-based Video Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Hierarchical Context Model (HCM) that enriches the object-based spatial context and relation-based temporal context based on clips. |
Meng Wei; Long Chen; Wei Ji; Xiaoyu Yue; Roger Zimmermann; | arxiv-cs.CV | 2023-07-18 |
137 | Relational Extraction on Wikipedia Tables Using Convolutional and Memory Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a new model consisting of Convolutional Neural Network (CNN) and Bidirectional-Long Short Term Memory (BiLSTM) network to encode entities and learn dependencies among them, respectively. |
Arif Shahriar; Rohan Saha; Denilson Barbosa; | arxiv-cs.CL | 2023-07-11 |
138 | Entity Identifier: A Natural Text Parsing-based Framework For Entity Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we use natural language processing techniques to extract structured information from requirements descriptions, in order to automate the generation of CRUD (Create, Read, Update, Delete) class code. To facilitate this process, we introduce a pipeline for extracting entity and relation information, as well as a representation called an Entity Tree to model this information. |
El Mehdi Chouham; Jessica López Espejel; Mahaman Sanoussi Yahaya Alassan; Walid Dahhane; El Hassane Ettifouri; | arxiv-cs.CL | 2023-07-10 |
139 | HistRED: A Historical Document-Level Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To demonstrate the usefulness of our dataset, we propose a bilingual RE model that leverages both Korean and Hanja contexts to predict relations between entities. |
Soyoung Yang; Minseok Choi; Youngwoo Cho; Jaegul Choo; | arxiv-cs.CL | 2023-07-09 |
140 | Linguistic Representations for Fewer-shot Relation Extraction Across Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. |
Sireesh Gururaja; Ritam Dutt; Tinglong Liao; Carolyn Ros�; | acl | 2023-07-08 |
141 | Did The Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Document-level relation extraction (DocRE) attracts more research interest recently. While models achieve consistent performance gains in DocRE, their underlying decision rules are still understudied: Do they make the right predictions according to rationales? In this paper, we take the first step toward answering this question and then introduce a new perspective on comprehensively evaluating a model. |
Haotian Chen; Bingsheng Chen; Xiangdong Zhou; | acl | 2023-07-08 |
142 | Revisiting Relation Extraction in The Era of Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. |
Somin Wadhwa; Silvio Amir; Byron Wallace; | acl | 2023-07-08 |
143 | S2ynRE: Two-stage Self-training with Synthetic Data for Low-resource Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose S2ynRE, a framework of two-stage Self-training with Synthetic data for Relation Extraction. |
BENFENG XU et. al. | acl | 2023-07-08 |
144 | Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a **J**oint **C**onstrained Learning framework with **B**oundary-adjusting for Emotion-Cause Pair Extraction (**JCB**). |
HUAWEN FENG et. al. | acl | 2023-07-08 |
145 | MultiTACRED: A Multilingual Version of The TAC Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al. , 2017). To address this gap, we introduce the MultiTACRED dataset, covering 12 typologically diverse languages from 9 language families, which is created by machine-translating TACRED instances and automatically projecting their entity annotations. |
Leonhard Hennig; Philippe Thomas; Sebastian M�ller; | acl | 2023-07-08 |
146 | No Clues Good Clues: Out of Context Lexical Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are indications that commonly used PTLMs already encode enough linguistic knowledge to allow the use of minimal (or none) textual context for some linguistically motivated tasks, thus notably reducing human effort, the need for data pre-processing, and favoring techniques that are language neutral since do not rely on syntactic structures. In this work, we explore this idea for the tasks of lexical relation classification (LRC) and graded Lexical Entailment (LE). |
Lucia Pitarch; Jordi Bernad; Lacramioara Dranca; Carlos Bobed Lisbona; Jorge Gracia; | acl | 2023-07-08 |
147 | Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Implicit discourse relation classification is a challenging task due to the absence of discourse connectives. To overcome this issue, we design an end-to-end neural model to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB. |
Wei Liu; Michael Strube; | acl | 2023-07-08 |
148 | RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a fine-grained semantic matching method tailored for zero-shot relation extraction. |
JUN ZHAO et. al. | acl | 2023-07-08 |
149 | Consistent Prototype Learning for Few-Shot Continual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a new N-way-K-shot Continual Relation Extraction (NK-CRE) task and propose a novel few-shot continual relation extraction method with Consistent Prototype Learning (ConPL) to address the aforementioned issues. |
Xiudi Chen; Hui Wu; Xiaodong Shi; | acl | 2023-07-08 |
150 | Open Set Relation Extraction Via Unknown-Aware Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an unknown-aware training method, regularizing the model by dynamically synthesizing negative instances that can provide the missing supervision signals. |
JUN ZHAO et. al. | acl | 2023-07-08 |
151 | Direct Fact Retrieval from Knowledge Graphs Without Entity Linking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this approach requires additional labels for training each of the three subcomponents in addition to pairs of input texts and facts, and also may accumulate errors propagated from failures in previous steps. To tackle these limitations, we propose a simple knowledge retrieval framework, which directly retrieves facts from the KGs given the input text based on their representational similarities, which we refer to as Direct Fact Retrieval (DiFaR). |
Jinheon Baek; Alham Fikri Aji; Jens Lehmann; Sung Ju Hwang; | acl | 2023-07-08 |
152 | Rethinking Multimodal Entity and Relation Extraction from A Translation Point of View Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We revisit the multimodal entity and relation extraction from a translation point of view. |
Changmeng Zheng; Junhao Feng; Yi Cai; Xiaoyong Wei; Qing Li; | acl | 2023-07-08 |
153 | Improving Continual Relation Extraction By Distinguishing Analogous Semantics Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct an empirical study on existing works and observe that their performance is severely affected by analogous relations. To address this issue, we propose a novel continual extraction model for analogous relations. |
Wenzheng Zhao; Yuanning Cui; Wei Hu; | acl | 2023-07-08 |
154 | Information Screening Whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and external-information exploiting. |
Shengqiong Wu; Hao Fei; Yixin Cao; Lidong Bing; Tat-Seng Chua; | acl | 2023-07-08 |
155 | Actively Supervised Clustering for Open Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel setting, named actively supervised clustering for OpenRE. |
JUN ZHAO et. al. | acl | 2023-07-08 |
156 | REDFM: A Filtered and Multilingual Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English. In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems. |
?Pere-Llu�s Huguet Cabot; Simone Tedeschi; Axel-Cyrille Ngonga Ngomo; Roberto Navigli; | acl | 2023-07-08 |
157 | UniEX: An Effective and Efficient Framework for Unified Information Extraction Via A Span-extractive Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. |
YANG PING et. al. | acl | 2023-07-08 |
158 | Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a Document-level distant Relation Extraction framework with Uncertainty Guided label denoising, UGDRE. |
QI SUN et. al. | acl | 2023-07-08 |
159 | Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve the prediction, this research proposes to retrieve textual and visual evidence based on the object, sentence, and whole image. |
Xuming Hu; Zhijiang Guo; Zhiyang Teng; Irwin King; Philip S. Yu; | acl | 2023-07-08 |
160 | A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications. To overcome this limitation, we propose TaG, a novel table-to-graph generation model for joint extractionof entities and relations at document-level. |
Ruoyu Zhang; Yanzeng Li; Lei Zou; | acl | 2023-07-08 |
161 | TAGPRIME: A Unified Framework for Relational Structure Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent works usually propose sophisticated models for each task independently and pay less attention to the commonality of these tasks and to have a unified framework for all the tasks. In this work, we propose to take a unified view of all these tasks and introduce TAGPRIME to address relational structure extraction problems. |
I-HUNG HSU et. al. | acl | 2023-07-08 |
162 | More Than Classification: A Unified Framework for Event Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs. |
QUZHE HUANG et. al. | acl | 2023-07-08 |
163 | Continual Contrastive Finetuning Improves Low-Resource Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim at bridging the gap and propose to pretrain and finetune the RE model using consistent objectives of contrastive learning. |
Wenxuan Zhou; Sheng Zhang; Tristan Naumann; Muhao Chen; Hoifung Poon; | acl | 2023-07-08 |
164 | UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the reformulation, we propose a Unified Token-pair Classification architecture for Information Extraction (UTC-IE), where we introduce Plusformer on top of the token-pair feature matrix. |
HANG YAN et. al. | acl | 2023-07-08 |
165 | Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate the issues, we propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction, which captures the global structure information between individual tasks and exploits interactions within unlabeled data. |
Yandan Zheng; Anran Hao; Anh Tuan Luu; | acl | 2023-07-08 |
166 | A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Though discourse parsing can help multiple NLP fields, there has been no wide language model search done on implicit discourse relation classification. This hinders researchers from fully utilizing public-available models in discourse analysis. |
Bruce W. Lee; BongSeok Yang; Jason Hyung-Jong Lee; | arxiv-cs.CL | 2023-07-07 |
167 | Linguistic Representations for Fewer-shot Relation Extraction Across Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. |
Sireesh Gururaja; Ritam Dutt; Tinglong Liao; Carolyn Rose; | arxiv-cs.CL | 2023-07-07 |
168 | IMETRE: Incorporating Markers of Entity Types for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we approach the task of relationship extraction in the financial dataset REFinD. |
N Harsha Vardhan; Manav Chaudhary; | arxiv-cs.CL | 2023-06-30 |
169 | GPT-FinRE: In-context Learning for Financial Relation Extraction Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In the financial domain, relation extraction plays a vital role in extracting valuable information from financial documents, such as news articles, earnings reports, and company filings. This paper describes our solution to relation extraction on one such dataset REFinD. |
Pawan Kumar Rajpoot; Ankur Parikh; | arxiv-cs.CL | 2023-06-30 |
170 | Exploring Self-Distillation Based Relational Reasoning Training for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to deal with the first problem, we propose a document-level RE model with a reasoning module that contains a core unit, the reasoning multi-head self-attention unit. |
LIANG ZHANG et. al. | aaai | 2023-06-26 |
171 | Sequence Generation with Label Augmentation for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. |
Bo Li; Dingyao Yu; Wei Ye; Jinglei Zhang; Shikun Zhang; | aaai | 2023-06-26 |
172 | Joint Multimodal Entity-Relation Extraction Based on Edge-Enhanced Graph Alignment Network and Word-Pair Relation Tagging IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for the JMERE task. |
Li Yuan; Yi Cai; Jin Wang; Qing Li; | aaai | 2023-06-26 |
173 | Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. |
Bo Li; Wei Ye; Jinglei Zhang; Shikun Zhang; | aaai | 2023-06-26 |
174 | Linking People Across Text and Images Based on Social Relation Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We observe that humans are adept at exploring social relations to assist identifying people. Therefore, we propose a Social Relation Reasoning (SRR) model to address the aforementioned issues. |
Yang Lei; Peizhi Zhao; Pijian Li; Yi Cai; Qingbao Huang; | aaai | 2023-06-26 |
175 | RankDNN: Learning to Rank for Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. |
QIANYU GUO et. al. | aaai | 2023-06-26 |
176 | Competition or Cooperation? Exploring Unlabeled Data Via Challenging Minimax Game for Semi-supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the consensus of two modules greatly restricts the model from exploring diverse relation expressions in unlabeled set, which hinders the performance as well as model generalization. To tackle this problem, in this paper, we propose a novel competition-based method AdvSRE. |
YU HONG et. al. | aaai | 2023-06-26 |
177 | Improving Distantly Supervised Relation Extraction By Natural Language Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. |
Kang Zhou; Qiao Qiao; Yuepei Li; Qi Li; | aaai | 2023-06-26 |
178 | A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, incomplete labeling in document-level RE has received increasing attention, and some studies have used methods such as positive-unlabeled learning to tackle this issue, but there is still a lot of room for improvement. Motivated by this, we propose a positive-augmentation and positive-mixup positive-unlabeled metric learning framework (P3M). |
Ye Wang; Huazheng Pan; Tao Zhang; Wen Wu; Wenxin Hu; | arxiv-cs.CL | 2023-06-26 |
179 | FmLRE: A Low-Resource Relation Extraction Model Based on Feature Mapping Similarity Calculation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing work takes advantages of self-training or distant supervision to expand the limited labeled data in the data-driven approaches, while the selection bias of pseudo labels may cause the error accumulation in subsequent relation classification. To address this issue, this paper proposes fmLRE, an iterative feedback method based on feature mapping similarity calculation to improve the accuracy of pseudo labels. |
Peng Wang; Tong Shao; Ke Ji; Guozheng Li; Wenjun Ke; | aaai | 2023-06-26 |
180 | KICE: A Knowledge Consolidation and Expansion Framework for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the collaboration among different knowledge sources and present KICE, a Knowledge-evolving framework by Iterative Consolidation and Expansion with the guidance of PLMs and rule-based patterns. |
Yilin Lu; Xiaoqiang Wang; Haofeng Yang; Siliang Tang; | aaai | 2023-06-26 |
181 | Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we explore distilling the reasoning ability of large language models (LLMs) into a more compact student model by generating a \textit{chain of thought} (CoT) — a sequence of intermediate reasoning steps. |
Feng Chen; Yujian Feng; | arxiv-cs.CL | 2023-06-25 |
182 | Mutually Guided Few-shot Learning for Relational Triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). |
Chengmei Yang; Shuai Jiang; Bowei He; Chen Ma; Lianghua He; | arxiv-cs.CL | 2023-06-23 |
183 | Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects, so as to mine more helpful information for the task, termed as DGF-PT. |
QIAN LI et. al. | arxiv-cs.CL | 2023-06-19 |
184 | BioREx: Improving Biomedical Relation Extraction By Leveraging Heterogeneous Datasets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. |
Po-Ting Lai; Chih-Hsuan Wei; Ling Luo; Qingyu Chen; Zhiyong Lu; | arxiv-cs.CL | 2023-06-19 |
185 | Semi-supervised Relation Extraction Via Data Augmentation and Consistency-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we leverage the recent advances in controlled text generation to perform high quality data augmentation for the RE task. |
Komal K. Teru; | arxiv-cs.CL | 2023-06-16 |
186 | RED$^{\rm FM}$: A Filtered and Multilingual Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English. In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems. |
Pere-Lluís Huguet Cabot; Simone Tedeschi; Axel-Cyrille Ngonga Ngomo; Roberto Navigli; | arxiv-cs.CL | 2023-06-16 |
187 | Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, traditional self-training is vulnerable to confirmation bias and exhibits poor performance in minority classes. To overcome this limitation, we proposed a novel class-adaptive re-sampling self-training framework. |
Qingyu Tan; Lu Xu; Lidong Bing; Hwee Tou Ng; | arxiv-cs.CL | 2023-06-16 |
188 | Rethinking Document-Level Relation Extraction: A Reality Check Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we do not aim at proposing a novel model for DocRE. |
Jing Li; Yequan Wang; Shuai Zhang; Min Zhang; | arxiv-cs.CL | 2023-06-15 |
189 | Building A Corpus for Biomedical Relation Extraction of Species Mentions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a manually annotated corpus, Species-Species Interaction, for extracting meaningful binary relations between species, in biomedical texts, at sentence level, with a focus on the gut microbiota. |
Oumaima El Khettari; Solen Quiniou; Samuel Chaffron; | arxiv-cs.CL | 2023-06-14 |
190 | Open Set Relation Extraction Via Unknown-Aware Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an unknown-aware training method, regularizing the model by dynamically synthesizing negative instances. |
JUN ZHAO et. al. | arxiv-cs.CL | 2023-06-08 |
191 | Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create a high-performing model. |
Fréjus A. A. Laleye; Loïc Rakotoson; Sylvain Massip; | arxiv-cs.CL | 2023-06-07 |
192 | FinRED: A Dataset for Relation Extraction in Financial Domain Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we release FinRED, a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. |
SOUMYA SHARMA et. al. | arxiv-cs.CL | 2023-06-06 |
193 | A Comprehensive Survey on Deep Learning for Relation Extraction: Recent Advances and New Frontiers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey provides a comprehensive review of existing deep learning techniques for RE. |
XIAOYAN ZHAO et. al. | arxiv-cs.CL | 2023-06-03 |
194 | GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. |
XUMING HU et. al. | arxiv-cs.CL | 2023-05-26 |
195 | ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. |
Trung Hoang Le; Huiping Cao; Tran Cao Son; | arxiv-cs.CL | 2023-05-24 |
196 | RE$^2$: Region-Aware Relation Extraction from Visually Rich Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose REgion-Aware Relation Extraction (RE$^2$) that leverages region-level spatial structure among the entity blocks to improve their relation prediction. |
Pritika Ramu; Sijia Wang; Lalla Mouatadid; Joy Rimchala; Lifu Huang; | arxiv-cs.CL | 2023-05-23 |
197 | Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The absence of global structure information may make the model struggle to learn long-range relations and easily predict conflicted results. To alleviate such limitations, we propose a GlObal Structure knowledge-guided relation Extraction (GOSE) framework. |
XIANGNAN CHEN et. al. | arxiv-cs.CL | 2023-05-23 |
198 | Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new approach, ReXMiner, for zero-shot relation extraction in web mining. |
Zilong Wang; Jingbo Shang; | arxiv-cs.CL | 2023-05-23 |
199 | SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energy-based Event-Centric Hyperspheres (SPEECH). |
Shumin Deng; Shengyu Mao; Ningyu Zhang; Bryan Hooi; | arxiv-cs.CL | 2023-05-22 |
200 | How Fragile Is Relation Extraction Under Entity Replacements? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we operate the random and type-constrained entity replacements over the RE instances in TACRED and evaluate the state-of-the-art RE models under the entity replacements. |
YIWEI WANG et. al. | arxiv-cs.CL | 2023-05-22 |
201 | Zero-shot Visual Relation Detection Via Composite Visual Cues from Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel method for zero-shot VRD: RECODE, which solves RElation detection via COmposite DEscription prompts. |
LIN LI et. al. | arxiv-cs.CV | 2023-05-21 |
202 | Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate an unbiased UD-based XRE transfer by constructing a type of code-mixed UD forest. |
Hao Fei; Meishan Zhang; Min Zhang; Tat-Seng Chua; | arxiv-cs.CL | 2023-05-20 |
203 | Silver Syntax Pre-training for Cross-Domain Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate intermediate pre-training specifically for RE. |
Elisa Bassignana; Filip Ginter; Sampo Pyysalo; Rob van der Goot; Barbara Plank; | arxiv-cs.CL | 2023-05-18 |
204 | Aligning Instruction Tasks Unlocks Large Language Models As Zero-Shot Relation Extractors IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We hypothesize that instruction-tuning has been unable to elicit strong RE capabilities in LLMs due to RE’s low incidence in instruction-tuning datasets, making up less than 1% of all tasks (Wang et al., 2022). To address this limitation, we propose QA4RE, a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets. |
Kai Zhang; Bernal Jiménez Gutiérrez; Yu Su; | arxiv-cs.CL | 2023-05-18 |
205 | Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Multi-CrossRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. |
Elisa Bassignana; Filip Ginter; Sampo Pyysalo; Rob van der Goot; Barbara Plank; | arxiv-cs.CL | 2023-05-18 |
206 | UniEX: An Effective and Efficient Framework for Unified Information Extraction Via A Span-extractive Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. |
PING YANG et. al. | arxiv-cs.CL | 2023-05-17 |
207 | An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. |
Zhao Xie; Tian Gao; Kewei Wu; Jiao Chang; | cvpr | 2023-05-17 |
208 | GeoLayoutLM: Geometric Pre-Training for Visual Information Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, we reveal another factor that limits the performance of RE lies in the objective gap between the pre-training phase and the fine-tuning phase for RE. To tackle these issues, we propose in this paper a multi-modal framework, named GeoLayoutLM, for VIE. |
Chuwei Luo; Changxu Cheng; Qi Zheng; Cong Yao; | cvpr | 2023-05-17 |
209 | About Evaluation of F1 Score for RECENT Relation Extraction System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This document contains a discussion of the F1 score evaluation used in the article ‘Relation Classification with Entity Type Restriction’ by Shengfei Lyu, Huanhuan Chen published on Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. |
Michał Olek; | arxiv-cs.CL | 2023-05-16 |
210 | Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The recent advancement of foundation models with billions of parameters trained in a self-supervised manner with large volumes of training data that can be adapted to a variety of downstream tasks has helped to demonstrate high performance on a large range of Natural Language Processing (NLP) tasks. In this context, one emerging paradigm is in-context learning where a language model is used as it is with a prompt that provides instructions and some examples to perform a task without changing the parameters of the model using traditional approaches such as fine-tuning. |
Hanieh Khorashadizadeh; Nandana Mihindukulasooriya; Sanju Tiwari; Jinghua Groppe; Sven Groppe; | arxiv-cs.CL | 2023-05-15 |
211 | Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Considering the ubiquity of noisy labels in real-world datasets, in this paper, we formalize a more practical learning scenario, termed as \textit{noisy-CRE}. |
TING WU et. al. | arxiv-cs.CL | 2023-05-11 |
212 | Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new model, namely SCKD, to accomplish the continual few-shot RE task. |
Xinyi Wang; Zitao Wang; Wei Hu; | arxiv-cs.CL | 2023-05-11 |
213 | Revisiting Relation Extraction in The Era of Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. |
Somin Wadhwa; Silvio Amir; Byron C. Wallace; | arxiv-cs.CL | 2023-05-08 |
214 | Enhancing Continual Relation Extraction Via Classifier Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. |
HEMING XIA et. al. | arxiv-cs.CL | 2023-05-08 |
215 | MultiTACRED: A Multilingual Version of The TAC Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACRED dataset, covering 12 typologically diverse languages from 9 language families, which is created by machine-translating TACRED instances and automatically projecting their entity annotations. |
Leonhard Hennig; Philippe Thomas; Sebastian Möller; | arxiv-cs.CL | 2023-05-08 |
216 | HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose HIORE, a new method for unified entity relation extraction. |
YIJUN WANG et. al. | arxiv-cs.CL | 2023-05-07 |
217 | Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, the resolution of these mentions into a single visual element is essential for high quality process models. We propose an extension to the PET dataset that incorporates information about linguistic references and a corresponding method for resolving them. |
Julian Neuberger; Lars Ackermann; Stefan Jablonski; | arxiv-cs.CL | 2023-05-06 |
218 | Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Insights discussed in this study can help DL stakeholders select techniques for building optimal knowledge-graph-based systems. |
Ming Jiang; Jennifer D’Souza; Sören Auer; J. Stephen Downie; | arxiv-cs.DL | 2023-05-03 |
219 | Think Rationally About What You See: Continuous Rationale Extraction for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. … |
Xuming Hu; Zhaochen Hong; Chenwei Zhang; Irwin King; Philip S. Yu; | ArXiv | 2023-05-02 |
220 | KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we demonstrate that KEPLMs without incorporating the topic entities will lead to insufficient entity interaction and biased (relation) word semantics. |
YICHUAN LI et. al. | arxiv-cs.CL | 2023-05-02 |
221 | How to Unleash The Power of Large Language Models for Few-shot Relation Extraction? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5 through exhaustive experiments. |
Xin Xu; Yuqi Zhu; Xiaohan Wang; Ningyu Zhang; | arxiv-cs.CL | 2023-05-02 |
222 | A Novel Pipelined End-to-end Relation Extraction Framework with Entity Mentions and Contextual Semantic Representation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
ZHAORAN LIU et. al. | Expert Syst. Appl. | 2023-05-01 |
223 | Granularity-Aware Area Prototypical Network With Bimargin Loss for Few Shot Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Classification is one of the most important tasks in text mining. Previous methods either require large-scale manually-annotated data or rely on distant supervision … |
Haopeng Ren; Yi Cai; Raymond Y. K. Lau; Ho-fung Leung; Qing Li; | IEEE Transactions on Knowledge and Data Engineering | 2023-05-01 |
224 | Constructing A Knowledge Graph from Textual Descriptions of Software Vulnerabilities in The National Vulnerability Database Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a new method for constructing a vulnerability knowledge graph from information in the National Vulnerability Database (NVD). |
Anders Mølmen Høst; Pierre Lison; Leon Moonen; | arxiv-cs.CR | 2023-04-30 |
225 | Towards Model Robustness: Generating Contextual Counterfactuals for Entities in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the problem of automatically generating CAD for RE tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. |
Mi Zhang; Tieyun Qian; Ting Zhang; Xin Miao; | www | 2023-04-29 |
226 | Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Compared with standard text, understanding dialogue is more challenging for machines as the dynamic and unexpected semantic changes in each turn. To model such inconsistent semantics, we propose a simple but effective Hierarchical Dialogue Understanding model, HiDialog. |
Xiao Liu; Jian Zhang; Heng Zhang; Fuzhao Xue; Yang You; | arxiv-cs.CL | 2023-04-29 |
227 | ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations. |
CHUNKIT CHAN et. al. | arxiv-cs.CL | 2023-04-28 |
228 | Mutually Guided Few-Shot Learning For Relational Triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). |
C. Yang; S. Jiang; B. He; C. Ma; L. He; | icassp | 2023-04-27 |
229 | Asymmetric Polynomial Loss for Multi-Label Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the imbalance between redundant negative samples and rare positive samples could degrade the model performance. In this paper, we propose an effective Asymmetric Polynomial Loss (APL) to mitigate the above issues. |
Y. Huang; J. Qi; X. Wang; Z. Lin; | icassp | 2023-04-27 |
230 | Spammer Detection on Short Video Applications: A New Challenge and Baselines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new challenge of spammer detection on short video applications, where the multi-modal information of videos and reviews plays a more critical role than the spam relation graph. |
M. Yi; D. Liang; R. Wang; Y. Ding; H. Lu; | icassp | 2023-04-27 |
231 | TABLEIE: Capturing The Interactions Among Sub-Tasks in Information Extraction Via Double Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a double-table framework, TableIE, to capture the interactions among IE sub-tasks as well as improve the model efficiency. |
J. Lin; R. Xu; B. Chang; | icassp | 2023-04-27 |
232 | CORSD: Class-Oriented Relational Self Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel training framework named Class-Oriented Relational Self Distillation (CORSD) to address the limitations. |
M. YU et. al. | icassp | 2023-04-27 |
233 | Commdre: Document-Level Relation Extraction with Self-Supervised Commonsense Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a self-supervised commonsense-enhanced DocRE model, called CommDRE, without external knowledge. |
R. LI et. al. | icassp | 2023-04-27 |
234 | DocRED-FE: A Document-Level Fine-Grained Entity and Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we construct a large-scale document-level fine-grained JERE dataset DocRED-FE, which improves DocRED with Fine-Grained Entity Type. |
H. WANG et. al. | icassp | 2023-04-27 |
235 | Relational Representation Learning for Zero-Shot Relation Extraction with Instance Prompting and Prototype Rectification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method based on Instance Prompting and Prototype Rectification (IPPR) to conduct relational representation learning for zeroshot relation extraction. |
B. Duan; X. Liu; S. Wang; Y. Xu; B. Xiao; | icassp | 2023-04-27 |
236 | BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conclusions : In this paper, we propose a novel end-to-end system for the construction of a biomedical knowledge graph from clinical textual using a variation of BERT models. |
AYOUB HARNOUNE et. al. | arxiv-cs.CL | 2023-04-21 |
237 | Prompt-Learning for Cross-Lingual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel XRE algorithm based on Prompt-Tuning, referred to as Prompt-XRE. |
CHIAMING HSU et. al. | arxiv-cs.CL | 2023-04-20 |
238 | Multi-task Learning for Few-shot Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
V. Moscato; Giuseppe Napolano; Marco Postiglione; Giancarlo Sperlí; | Artificial Intelligence Review | 2023-04-19 |
239 | LED: A Dataset for Life Event Extraction from Dialogs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Life Event Dialog, a dataset containing fine-grained life event annotations on conversational data. |
Yi-Pei Chen; An-Zi Yen; Hen-Hsen Huang; Hideki Nakayama; Hsin-Hsi Chen; | arxiv-cs.CL | 2023-04-17 |
240 | Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains. |
CHENG DENG et. al. | arxiv-cs.IR | 2023-04-14 |
241 | Zero-shot Temporal Relation Extraction with ChatGPT IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate ChatGPT’s ability on zero-shot temporal relation extraction. |
Chenhan Yuan; Qianqian Xie; Sophia Ananiadou; | arxiv-cs.CL | 2023-04-11 |
242 | Sentence-Level Relation Extraction Via Contrastive Learning with Descriptive Relation Prompts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new paradigm, Contrastive Learning with Descriptive Relation Prompts(CTL-DRP), to jointly consider entity information, relational knowledge and entity type restrictions. |
Jiewen Zheng; Ze Chen; | arxiv-cs.CL | 2023-04-10 |
243 | Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we conduct a comprehensive evaluation of state-of-the-art LLMs, namely GPT-3.5, GPT-4, and Bard, within the realm of clinical language understanding tasks. |
Yuqing Wang; Yun Zhao; Linda Petzold; | arxiv-cs.CL | 2023-04-09 |
244 | Learning Implicit and Explicit Multi-task Interactions for Information Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Information extraction aims at extracting entities, relations, and so on, in text to support information retrieval systems. To extract information, researchers have considered … |
Kai Sun; Richong Zhang; Samuel Mensah; Yongyi Mao; Xudong Liu; | ACM Transactions on Information Systems | 2023-04-08 |
245 | Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE by Multi-Modal representation learning with Information Bottleneck (MMIB). |
SHIYAO CUI et. al. | arxiv-cs.MM | 2023-04-05 |
246 | Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study investigates the performance of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical tasks beyond question-answering. |
SHAN CHEN et. al. | arxiv-cs.CL | 2023-04-05 |
247 | EDeR: A Dataset for Exploring Dependency Relations Between Events Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We argue that an important type of relation not explored in NLP or IR research to date is that of an event being an argument – required or optional – of another event. We introduce the human-annotated Event Dependency Relation dataset (EDeR) which provides this dependency relation. |
Ruiqi Li; Patrik Haslum; Leyang Cui; | arxiv-cs.CL | 2023-04-04 |
248 | End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this effort, we employ a span-based pipeline approach to produce a new state-of-the-art E2ERE performance on the ChemProt dataset, resulting in $> 4\%$ improvement in F1-score over the prior best effort. |
Xuguang Ai; Ramakanth Kavuluru; | arxiv-cs.CL | 2023-04-03 |
249 | Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we aim to achieve better integration of both the discriminability and robustness for the DocRE problem. |
Jia Guo; Stanley Kok; Lidong Bing; | arxiv-cs.CL | 2023-04-03 |
250 | TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The latest trigger-enhanced methods propose trigger prediction tasks to promote DRE. |
Hao An; Dongsheng Chen; Weiyuan Xu; Zhihong Zhu; Yuexian Zou; | arxiv-cs.CL | 2023-03-29 |
251 | End-to-End $n$-ary Relation Extraction for Combination Drug Therapies Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This is an absolute $\approx 5\%$ F1-score improvement even over the prior best relation classification score with spotted drug entities (hence, not end-to-end). Thus our effort introduces a state-of-the-art first model for end-to-end extraction that is already superior to the best prior non end-to-end model for this task. |
Yuhang Jiang; Ramakanth Kavuluru; | arxiv-cs.CL | 2023-03-29 |
252 | Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To better glue the cross-modal semantics therein, we capture hinting features from user comments, which are retrieved via jointly leveraging visual and lingual similarity. |
Chunpu Xu; Jing Li; | arxiv-cs.CL | 2023-03-27 |
253 | Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the direct generation of radiology graphs from chest X-ray (CXR) images has not been attempted. To address this gap, we propose a novel approach called Prior-RadGraphFormer that utilizes a transformer model with prior knowledge in the form of a probabilistic knowledge graph (PKG) to generate radiology graphs directly from CXR images. |
YIHENG XIONG et. al. | arxiv-cs.CV | 2023-03-24 |
254 | DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we construct a large-scale document-level fine-grained JERE dataset DocRED-FE, which improves DocRED with Fine-Grained Entity Type. |
HONGBO WANG et. al. | arxiv-cs.CL | 2023-03-20 |
255 | STIXnet: A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents STIXnet, the first solution for the automated extraction of all STIX entities and relationships in CTI reports. |
Francesco Marchiori; Mauro Conti; Nino Vincenzo Verde; | arxiv-cs.IR | 2023-03-17 |
256 | GCRE-GPT: A Generative Model for Comparative Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that comparative relations can be directly extracted with high accuracy, by generative model. |
Yequan Wang; Hengran Zhang; Aixin Sun; Xuying Meng; | arxiv-cs.CL | 2023-03-15 |
257 | Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We perform error analyses and examine how different prompting strategies affect the performance of MRC models. |
CHENG PENG et. al. | arxiv-cs.CL | 2023-03-14 |
258 | Knowledge-augmented Few-shot Visual Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, humans have the ability to learn new relationships with just few examples based on their knowledge. Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD. |
TIANYU YU et. al. | arxiv-cs.CV | 2023-03-09 |
259 | Dynamic Multi-View Fusion Mechanism For Chinese Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods tend to ignore the internal information of the Chinese character and cannot filter out the noisy information of external knowledge. To address these issues, we propose a mixture-of-view-experts framework (MoVE) to dynamically learn multi-view features for Chinese relation extraction. |
JING YANG et. al. | arxiv-cs.CL | 2023-03-09 |
260 | Does Synthetic Data Generation of LLMs Help Clinical Text Mining? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, their effectiveness in the healthcare sector remains uncertain. In this study, we seek to investigate the potential of ChatGPT to aid in clinical text mining by examining its ability to extract structured information from unstructured healthcare texts, with a focus on biological named entity recognition and relation extraction. |
Ruixiang Tang; Xiaotian Han; Xiaoqian Jiang; Xia Hu; | arxiv-cs.CL | 2023-03-07 |
261 | Document-level Relation Extraction with Cross-sentence Reasoning Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, existing studies only focus on entity-level reasoning paths without considering global interactions among entities cross-sentence. To these ends, we propose a novel document-level RE model with a GRaph information Aggregation and Cross-sentence Reasoning network (GRACR). |
Hongfei Liu; Zhao Kang; Lizong Zhang; Ling Tian; Fujun Hua; | arxiv-cs.CL | 2023-03-07 |
262 | Detection of Generative Linguistic Steganography Based on Explicit and Latent Text Word Relation Mining Using Deep Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Covert communication channels can be easily constructed using text steganography based on social media. Offenders can easily utilize these channels to engage in various criminal … |
Songbin Li; Jingang Wang; Peng Liu; | IEEE Transactions on Dependable and Secure Computing | 2023-03-01 |
263 | Few-shot Relation Classification Using Clustering-based Prototype Modification Related Papers Related Patents Related Grants Related Venues Related Experts View |
Mingtong Wen; Tingyu Xia; Bowen Liao; Yuan Tian; | Knowl. Based Syst. | 2023-03-01 |
264 | 90% F1 Score in Relational Triple Extraction: Is It Real ? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a benchmark study of state-of-the-art joint entity and relation extraction models under a more realistic setting. |
Pratik Saini; Samiran Pal; Tapas Nayak; Indrajit Bhattacharya; | arxiv-cs.CL | 2023-02-20 |
265 | DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations. This work aims at addressing these issues to improve the usage of ER in DocRE. |
Youmi Ma; An Wang; Naoaki Okazaki; | arxiv-cs.CL | 2023-02-16 |
266 | LabelPrompt: Effective Prompt-based Learning for Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, associating natural language words that fill the masked token with semantic relation labels (\textit{e.g.} \textit{“org:founded\_by}”) is difficult. To address this challenge, this paper presents a novel prompt-based learning method, namely LabelPrompt, for the relation classification task. |
Wenjie Zhang; Xiaoning Song; Zhenhua Feng; Tianyang Xu; Xiaojun Wu; | arxiv-cs.CL | 2023-02-15 |
267 | Event Temporal Relation Extraction with Bayesian Translational Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce Bayesian-Trans, a Bayesian learning-based method that models the temporal relation representations as latent variables and infers their values via Bayesian inference and translational functions. |
Xingwei Tan; Gabriele Pergola; Yulan He; | arxiv-cs.CL | 2023-02-09 |
268 | Review of Entity Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out … |
Meimei Tuo; Wenzhong Yang; | J. Intell. Fuzzy Syst. | 2023-02-09 |
269 | Image Caption Generation Using Visual Attention Prediction and Contextual Spatial Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Reshmi Sasibhooshan; S. Kumaraswamy; Santhoshkumar Sasidharan; | Journal of Big Data | 2023-02-08 |
270 | Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an efficient end-to-end model, the Joint-NER-RE-Fourier (JNRF), to jointly learn the tasks of named entity recognition and relation extraction for documents of variable length. |
Anthony Yazdani; Dimitrios Proios; Hossein Rouhizadeh; Douglas Teodoro; | arxiv-cs.CL | 2023-02-08 |
271 | FGSI: Distant Supervision for Relation Extraction Method Based on Fine-Grained Semantic Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose that the key semantic information within a sentence plays a key role in the relationship extraction of entities. |
CHENGHONG SUN et. al. | arxiv-cs.CL | 2023-02-03 |
272 | Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. |
Kaifeng Gao; Long Chen; Hanwang Zhang; Jun Xiao; Qianru Sun; | arxiv-cs.CV | 2023-02-01 |
273 | Relational Distance and Document-level Contrastive Pre-training Based Relation Extraction Model Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yi Dong; Xiaolong Xu; | Pattern Recognit. Lett. | 2023-02-01 |
274 | Document-level Relation Extraction with Two-stage Dynamic Graph Attention Networks Related Papers Related Patents Related Grants Related Venues Related Experts View |
QI SUN et. al. | Knowl. Based Syst. | 2023-02-01 |
275 | Weakly-Supervised Questions for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this method required manually creating gold question templates for each new relation. Here, we do away with these gold templates and instead learn a model that can generate questions for unseen relations. |
Saeed Najafi; Alona Fyshe; | arxiv-cs.CL | 2023-01-21 |
276 | Learning Trajectory-Conditioned Relations to Predict Pedestrian Crossing Behavior Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Pedestrian intent, defined as future action, e.g., start crossing, can be dependent on traffic surroundings. In this paper, we develop a framework to incorporate such dependency given observed pedestrian trajectory and scene frames. |
Chen Zhou; Ghassan AlRegib; Armin Parchami; Kunjan Singh; | arxiv-cs.CV | 2023-01-13 |
277 | Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we fully utilize the models of all clients and propose a novel concept of \textit{major classifier vectors}, where a group of class vectors is obtained in an ensemble rather than the weighted average method on the server. |
Chunhui Du; Hao He; Yaohui Jin; | arxiv-cs.AI | 2023-01-12 |
278 | Multilingual Entity and Relation Extraction from Unified to Language-specific Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages. |
ZIXIANG WANG et. al. | arxiv-cs.CL | 2023-01-11 |
279 | API Entity and Relation Joint Extraction from Text Via Dynamic Prompt-tuned Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast to matching or labeling API entities and relations, this paper formulates heterogeneous API extraction and API relation extraction task as a sequence-to-sequence generation task, and proposes AERJE, an API entity-relation joint extraction model based on the large pre-trained language model. |
QING HUANG et. al. | arxiv-cs.SE | 2023-01-10 |
280 | ReVoLT: Relational Reasoning and Voronoi Local Graph Planning for Target-driven Navigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most of them obtain robot actions directly from observations in an end-to-end manner via an incomplete relation graph, which is not interpretable and reliable. We decouple this task and propose ReVoLT, a hierarchical framework: (a) an object detection visual front-end, (b) a high-level reasoner (infers semantic sub-goals), (c) an intermediate-level planner (computes geometrical positions), and (d) a low-level controller (executes actions). |
Junjia Liu; Jianfei Guo; Zehui Meng; Jingtao Xue; | arxiv-cs.RO | 2023-01-06 |
281 | Position-Aware Attention Mechanism–Based Bi-graph for Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Guiduo Duan; Yunrui Dong; Jiayu Miao; Tianxi Huang; | Cognitive Computation | 2023-01-01 |
282 | Reinforcement Learning-based Distant Supervision Relation Extraction for Fault Diagnosis Knowledge Graph Construction Under Industry 4.0 IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
C. CHEN et. al. | Adv. Eng. Informatics | 2023-01-01 |
283 | Chain-of-Thought Prompt Distillation for Multimodal Named Entity and Multimodal Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE) necessitate the fundamental reasoning capacity for intricate linguistic and multimodal … |
F. Chen; Yujian Feng; | ArXiv | 2023-01-01 |
284 | Learning Robust Representations for Continual Relation Extraction Via Adversarial Class Augmentation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Most previous work attributes catastrophic forgetting to the corruption of the learned representations as new relations come, with an implicit assumption that the CRE models have adequately learned the old relations. In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process. |
PEIYI WANG et. al. | emnlp | 2022-12-30 |
285 | Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we introduce cross-stitch bi-encoders, which allow full interaction between the text encoder and the KG encoder via a cross-stitch mechanism. |
Qin Dai; Benjamin Heinzerling; Kentaro Inui; | emnlp | 2022-12-30 |
286 | Graph-based Model Generation for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing models follow a ?one-for-all? scheme where one general large model performs all individual N-way-K-shot tasks in FSRE, which prevents the model from achieving the optimal point on each task. In view of this, we propose a model generation framework that consists of one general model for all tasks and many tiny task-specific models for each individual task. |
Wanli Li; Tieyun Qian; | emnlp | 2022-12-30 |
287 | Entity-centered Cross-document Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, they utilize all the text paths in a document bag in a coarse-grained way, without considering the connections between these text paths. In this paper, we aim to address both of these shortages and push the state-of-the-art for cross-document RE. |
FENGQI WANG et. al. | emnlp | 2022-12-30 |
288 | Boosting Document-Level Relation Extraction By Mining and Injecting Logical Rules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MILR, a logic enhanced framework that boosts DocRE by Mining and Injecting Logical Rules. |
Shengda Fan; Shasha Mo; Jianwei Niu; | emnlp | 2022-12-30 |
289 | MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few pre-defined relational instances as well as mine the specific meanings for cluster interpretability. |
JIAXIN WANG et. al. | emnlp | 2022-12-30 |
290 | Fine-grained Contrastive Learning for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose fine-grained contrastive learning (FineCL) for RE, which leverages fine-grained information about which silver labels are and are not noisy to improve the quality of learned relationship representations for RE. |
William Hogan; Jiacheng Li; Jingbo Shang; | emnlp | 2022-12-30 |
291 | Better Few-Shot Relation Extraction with Label Prompt Dropout Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, we present a novel approach called label prompt dropout, which randomly removes label descriptions in the learning process. |
Peiyuan Zhang; Wei Lu; | emnlp | 2022-12-30 |
292 | A Dataset for Hyper-Relational Extraction and A Cube-Filling Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers. |
Yew Ken Chia; Lidong Bing; Sharifah Mahani Aljunied; Luo Si; Soujanya Poria; | emnlp | 2022-12-30 |
293 | RelU-Net: Syntax-aware Graph U-Net for Relational Triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is due to the absence of entity locations, which is the prerequisite for pruning noisy edges from the dependency tree, when extracting relational triples. In this paper, we propose a unified framework to tackle this challenge and incorporate syntactic information for relational triple extraction. |
Yunqi Zhang; Yubo Chen; Yongfeng Huang; | emnlp | 2022-12-30 |
294 | Towards Relation Extraction from Speech Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new listening information extraction task, i. e. , speech relation extraction. |
TONGTONG WU et. al. | emnlp | 2022-12-30 |
295 | Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore methods to make better use of the multilingual annotation and language agnostic property of KG triples, and present novel knowledge based multilingual language models (KMLMs) trained directly on the knowledge triples. |
LINLIN LIU et. al. | emnlp | 2022-12-30 |
296 | Open Relation and Event Type Discovery with Type Abstraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This calls for systems that can automatically infer new types from given corpora, a task which we refer to as type discovery. To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type. |
Sha Li; Heng Ji; Jiawei Han; | emnlp | 2022-12-30 |
297 | Towards Better Document-level Relation Extraction Via Iterative Inference Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing methods usually directly predict the relations of all entity pairs of input document in a one-pass manner, ignoring the fact that predictions of some entity pairs heavily depend on the predicted results of other pairs. To deal with this issue, in this paper, we propose a novel document-level RE model with iterative inference. |
LIANG ZHANG et. al. | emnlp | 2022-12-30 |
298 | Multilingual Relation Classification Via Efficient and Effective Prompting IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the first work on prompt-based multilingual relation classification (RC), by introducing an efficient and effective method that constructs prompts from relation triples and involves only minimal translation for the class labels. |
Yuxuan Chen; David Harbecke; Leonhard Hennig; | emnlp | 2022-12-30 |
299 | MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. |
XIAOZHI WANG et. al. | emnlp | 2022-12-30 |
300 | ReSel: N-ary Relation Extraction from Scientific Text and Tables By Learning to Retrieve and Select IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study the problem of extracting N-ary relation tuples from scientific articles. |
YUCHEN ZHUANG et. al. | emnlp | 2022-12-30 |
301 | Revisiting DocRED – Addressing The False Negative Problem in Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we find that the annotation of DocRED is incomplete, i.e., false negative samples are prevalent. We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset. |
Qingyu Tan; Lu Xu; Lidong Bing; Hwee Tou Ng; Sharifah Mahani Aljunied; | emnlp | 2022-12-30 |
302 | Large Language Models Are Few-shot Clinical Information Extractors IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that large language models, such as InstructGPT (Ouyang et al., 2022), perform well at zero- and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain. |
Monica Agrawal; Stefan Hegselmann; Hunter Lang; Yoon Kim; David Sontag; | emnlp | 2022-12-30 |
303 | Query-based Instance Discrimination Network for Relational Triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they still suffer from error propagation, relation redundancy and lack of high-level connections between triples. To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples. |
ZEQI TAN et. al. | emnlp | 2022-12-30 |
304 | A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To solve the common incomplete labeling problem, we propose a unified positive-unlabeled learning framework – shift and squared ranking loss positive-unlabeled (SSR-PU) learning. |
Ye Wang; Xinxin Liu; Wenxin Hu; Tao Zhang; | emnlp | 2022-12-30 |
305 | Multi-hop Evidence Retrieval for Cross-document Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This inevitably brings the challenge of efficient open-space evidence retrieval to support the inference of cross-document relations, along with the challenge of multi-hop reasoning on top of entities and evidence scattered in an open set of documents. To combat these challenges, we propose MR.COD (Multi-hop evidence retrieval for Cross-document relation extraction), which is a multi-hop evidence retrieval method based on evidence path mining and ranking. |
Keming Lu; I-Hung Hsu; Wenxuan Zhou; Mingyu Derek Ma; Muhao Chen; | arxiv-cs.CL | 2022-12-21 |
306 | Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present NBR, which converts biomedical RE as natural language inference formulation through indirect supervision. |
Jiashu Xu; Mingyu Derek Ma; Muhao Chen; | arxiv-cs.CL | 2022-12-21 |
307 | Integrating Heterogeneous Domain Information Into Relation Extraction: A Case Study on Drug-Drug Interaction Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The proposed model is trained and evaluated on a widely used data set, and as a result, it is shown that utilizing heterogeneous domain information significantly improves the performance of relation extraction from the literature. |
Masaki Asada; | arxiv-cs.CL | 2022-12-20 |
308 | Document-level Relation Extraction with Relation Correlations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. |
Ridong Han; Tao Peng; Benyou Wang; Lu Liu; Xiang Wan; | arxiv-cs.CL | 2022-12-20 |
309 | Enriching Relation Extraction with OpenIE Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE by encoding structured information about the sentences’ principal units, such as subjects, objects, verbal phrases, and adverbials, into various forms of vectorized (and hence unstructured) representations of the sentences. |
Alessandro Temperoni; Maria Biryukov; Martin Theobald; | arxiv-cs.CL | 2022-12-19 |
310 | Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature Using Attention-based Relational Context Information Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Because protein-protein interactions (PPIs) are crucial to understand living systems, harvesting these data is essential to probe disease development and discern gene/protein … |
Gilchan Park; S. McCorkle; Carlos Soto; Ian K. Blaby; Shinjae Yoo; | 2022 IEEE International Conference on Big Data (Big Data) | 2022-12-17 |
311 | Uncertainty-guided Mutual Consistency Training for Semi-supervised Biomedical Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Biomedical relation extraction seeks to automatically extract biomedical relations from biomedical text, which plays an important role in biomedical studies. However, constructing … |
BING MAO et. al. | 2022 IEEE International Conference on Bioinformatics and … | 2022-12-06 |
312 | Location-Guided Token Pair Tagger for Joint Biomedical Entity and Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Automatic extraction of biomedical relations is important for many tasks, such as drug discovery, protein prediction. However, since the biomedical corpus contains a large number … |
Yan Zhang; Jiru Li; Zhihao Yang; Hongfei Lin; Jian Wang; | 2022 IEEE International Conference on Bioinformatics and … | 2022-12-06 |
313 | Syntactic Type-aware Graph Attention Network for Drug-drug Interactions and Their Adverse Effects Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Automatic extraction of drug-drug interactions and their adverse effects can promote the research of pharmacovigilance and thus attracts attention from both academia and industry. … |
PENG CHEN et. al. | 2022 IEEE International Conference on Bioinformatics and … | 2022-12-06 |
314 | Cross-Domain Few-Shot Relation Extraction Via Representation Learning and Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous metric-based few-shot relation extraction algorithms identify relationships by comparing the prototypes generated by the few labeled sentences embedding with the embeddings of the query sentences using a trained metric function. |
Zhongju Yuan; Zhenkun Wang; Genghui Li; | arxiv-cs.CL | 2022-12-05 |
315 | Named Entity and Relation Extraction with Multi-Modal Retrieval IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Multi-modal Retrieval based framework (MoRe). |
XINYU WANG et. al. | arxiv-cs.CL | 2022-12-03 |
316 | A Prototype Network Enhanced Relation Semantic Representation for Few-shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Haitao He; Haoran Niu; Jianzhou Feng; Qian Wang; Qikai Wei; | Human-Centric Intelligent Systems | 2022-12-02 |
317 | SwitchNet: A Modular Neural Network for Adaptive Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
HONGYIN ZHU et. al. | Comput. Electr. Eng. | 2022-12-01 |
318 | A Span-based Multi-Modal Attention Network for Joint Entity-relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qian Wan; Luona Wei; Shan Zhao; Jie Liu; | Knowl. Based Syst. | 2022-12-01 |
319 | Semi Supervised Approach for Relation Extraction in Agriculture Documents Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this work, we propose a semi-supervised boot-strapping approach for relation extraction in domain specific texts, specifically focusing on agricultural domain. Our approach … |
V. G; Deepa Gupta; Vani Kanjirangat; | 2022 OITS International Conference on Information … | 2022-12-01 |
320 | Multi-Relation Extraction Via A Global-Local Graph Convolutional Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) extracts the semantic relations among entities in a sentence, which converts the unstructured text into structured and easy-to-understand information. … |
Harry Cheng; Lizi Liao; Linmei Hu; Liqiang Nie; | IEEE Transactions on Big Data | 2022-12-01 |
321 | The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: With the development of the social Internet of Things (IoT) and multimedia communications, our daily lives in computational social systems have become more convenient; for … |
Honghao Gao; Jiadong Huang; Yuan Tao; Walayat Hussain; Yuzhen Huang; | IEEE Transactions on Computational Social Systems | 2022-12-01 |
322 | RankDNN: Learning to Rank for Few-shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. |
QIANYU GUO et. al. | arxiv-cs.CV | 2022-11-28 |
323 | Towards Better Document-level Relation Extraction Via Iterative Inference Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing methods usually directly predict the relations of all entity pairs of input document in a one-pass manner, ignoring the fact that predictions of some entity pairs heavily depend on the predicted results of other pairs. To deal with this issue, in this paper, we propose a novel document-level RE model with iterative inference. |
LIANG ZHANG et. al. | arxiv-cs.CL | 2022-11-25 |
324 | Learning with Silver Standard Data for Zero-shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to first detect a small amount of clean data from silver standard data and then use the selected clean data to finetune the pretrained model. |
Tianyin Wang; Jianwei Wang; Ziqian Zeng; | arxiv-cs.CL | 2022-11-24 |
325 | On Analyzing The Role of Image for Visual-enhanced Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we take an in-depth empirical analysis that indicates the inaccurate information in the visual scene graph leads to poor modal alignment weights, further degrading performance. |
LEI LI et. al. | arxiv-cs.CL | 2022-11-14 |
326 | Unimodal and Multimodal Representation Training for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite being less effective, we highlight circumstances where visual information can bolster performance. |
CIARAN COONEY et. al. | arxiv-cs.CL | 2022-11-11 |
327 | Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate confirmation bias due to the lack of feedback loops in existing LRIE learning paradigms, we develop a Gradient Imitation Reinforcement Learning (GIRL) method to encourage pseudo-labeled data to imitate the gradient descent direction on labeled data, which can force pseudo-labeled data to achieve better optimization capabilities similar to labeled data. |
XUMING HU et. al. | arxiv-cs.CL | 2022-11-11 |
328 | Towards Automating Numerical Consistency Checks in Financial Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce KPI-Check, a novel system that automatically identifies and cross-checks semantically equivalent key performance indicators (KPIs), e.g. revenue or total costs, in real-world German financial reports. |
LARS HILLEBRAND et. al. | arxiv-cs.CL | 2022-11-11 |
329 | Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To better capture and exploit instructive information, we propose a novel expLicit syntAx Refinement and Subsentence mOdeliNg based framework (LARSON). |
Zhichao Duan; Xiuxing Li; Zhenyu Li; Zhuo Wang; Jianyong Wang; | arxiv-cs.CL | 2022-11-10 |
330 | Active Relation Discovery: Towards General and Label-aware Open Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods cannot label human-readable and meaningful types for novel relations, which is urgently required by the downstream tasks. To address these issues, we propose the Active Relation Discovery (ARD) framework, which utilizes relational outlier detection for discriminating known and novel relations and involves active learning for labeling novel relations. |
YANGNING LI et. al. | arxiv-cs.CL | 2022-11-08 |
331 | SelfMatch: Robust Semisupervised Time‐series Classification with Self‐distillation IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Over the years, a number of semisupervised deep‐learning algorithms have been proposed for time‐series classification (TSC). In semisupervised deep learning, from the point of … |
HUANLAI XING et. al. | International Journal of Intelligent Systems | 2022-11-01 |
332 | Distantly Supervised Relation Extraction with KB-enhanced Reconstructed Latent Iterative Graph Networks Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qiji Zhou; Yue Zhang; Donghong Ji; | Knowl. Based Syst. | 2022-11-01 |
333 | Can We Have Both Fish and Bear’s Paw?: Improving Performance, Reliability, and Both of Them for Relation Extraction Under Label Shift Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we make contributions by answering the following three questions: 1) How to improve performance of DS-RE models under label shift? |
YU HONG et. al. | cikm | 2022-10-29 |
334 | PromptORE – A Novel Approach Towards Fully Unsupervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate the reliance on hyperparameters, we propose PromptORE, a "Prompt-based Open Relation Extraction" model. |
Pierre-Yves Genest; Pierre-Edouard Portier; Elöd Egyed-Zsigmond; Laurent-Walter Goix; | cikm | 2022-10-29 |
335 | Co-Training with Validation: A Generic Framework for Semi-Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a generic SRE paradigm, called Co-Training with Validation (CTV), for making full use of learners to benefit more from unlabeled corpus. |
Shun Zhang; Xiangkui Lu; Jun Wu; | cikm | 2022-10-29 |
336 | Evidence-aware Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, there often exist strong semantic correlations between evidence sentences that collaborate together to describe a specific relation. To address these challenges, we propose a novel evidence-aware model for document-level RE. |
TIANYU XU et. al. | cikm | 2022-10-29 |
337 | Improving Graph-based Document-Level Relation Extraction Model with Novel Graph Structure Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, there is a possibility of losing lexical information of the relations among entities directly expressed in a sentence. To address this issue, we propose two novel graph structures: an anaphoric graph and a local-context graph. |
Seongsik Park; Dongkeun Yoon; Harksoo Kim; | cikm | 2022-10-29 |
338 | DORE: Document Ordered Relation Extraction Based on Generative Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models. |
Qipeng Guo; Yuqing Yang; Hang Yan; Xipeng Qiu; Zheng Zhang; | arxiv-cs.CL | 2022-10-28 |
339 | ReSel: N-ary Relation Extraction from Scientific Text and Tables By Learning to Retrieve and Select IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study the problem of extracting N-ary relation tuples from scientific articles. |
YUCHEN ZHUANG et. al. | arxiv-cs.CL | 2022-10-25 |
340 | Augmenting Task-Oriented Dialogue Systems with Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose integration of relation extraction into this pipeline as an effective way to expand the capabilities of dialogue systems. |
Andrew Lee; Zhenguo Chen; Kevin Leach; Jonathan K. Kummerfeld; | arxiv-cs.CL | 2022-10-24 |
341 | Span-based Joint Entity and Relation Extraction Augmented with Sequence Tagging Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Sequence Tagging augmented Span-based Network (STSN), a span-based joint model that can make use of token-level label information. |
BIN JI et. al. | arxiv-cs.CL | 2022-10-23 |
342 | PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce four natural language processing tasks: sentence classification, named entity recognition, relation classification, and joint extraction of entities and relations. |
XIANJUN YANG et. al. | arxiv-cs.CL | 2022-10-22 |
343 | Generative Prompt Tuning for Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the text infilling task for pre-training generative models that can flexibly predict missing spans, we propose a novel generative prompt tuning method to reformulate relation classification as an infilling problem, which frees our approach from limitations of current prompt based approaches and thus fully exploits rich semantics of entity and relation types. |
Jiale Han; Shuai Zhao; Bo Cheng; Shengkun Ma; Wei Lu; | arxiv-cs.CL | 2022-10-22 |
344 | Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a simple enhancement of RE using $k$ nearest neighbors ($k$NN-RE). |
ZHEN WAN et. al. | arxiv-cs.CL | 2022-10-21 |
345 | Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a multi-view consistent contrastive learning for a more complete semantics-to-equation mapping. |
WENQI ZHANG et. al. | arxiv-cs.CL | 2022-10-20 |
346 | Pre-trained Sentence Embeddings for Implicit Discourse Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore the utility of pre-trained sentence embeddings as base representations in a neural network for implicit discourse relation sense classification. |
Murali Raghu Babu Balusu; Yangfeng Ji; Jacob Eisenstein; | arxiv-cs.CL | 2022-10-20 |
347 | Knowledge-Enhanced Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, to our best knowledge, there is currently no public dataset available that encompasses both evidence sentences and knowledge graphs for knowledge-enhanced relation extraction. To address this gap, we introduce the Knowledge-Enhanced Relation Extraction Dataset (KERED). |
YUCONG LIN et. al. | arxiv-cs.LG | 2022-10-19 |
348 | CEntRE: A Paragraph-level Chinese Dataset for Relation Extraction Among Enterprises Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To encourage further progress in the research, we introduce the CEntRE, a new dataset constructed from publicly available business news data with careful human annotation and intelligent data processing. |
PEIPEI LIU et. al. | arxiv-cs.CL | 2022-10-19 |
349 | Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents an empirical study to build relation extraction systems in low-resource settings. |
XIN XU et. al. | arxiv-cs.CL | 2022-10-19 |
350 | KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents and link them to their numerical values and other attributes. |
TOBIAS DEUSSER et. al. | arxiv-cs.CL | 2022-10-17 |
351 | CrossRE: A Cross-Domain Dataset for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Little is known on how well a RE system fares in challenging, but realistic out-of-distribution evaluation setups. To address this gap, we propose CrossRE, a new, freely-available cross-domain benchmark for RE, which comprises six distinct text domains and includes multi-label annotations. |
Elisa Bassignana; Barbara Plank; | arxiv-cs.CL | 2022-10-17 |
352 | RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To generalize to new relations more effectively, this paper proposes a novel pipeline for the FSRE task based on queRy-information guided Attention and adaptive Prototype fuSion, namely RAPS. |
Yuzhe Zhang; Min Cen; Tongzhou Wu; Hong Zhang; | arxiv-cs.CL | 2022-10-15 |
353 | Confidence Estimation of Classification Based on The Distribution of The Neural Network Output Layer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose novel methods that, given a neural network classification model, estimate uncertainty of particular predictions generated by this model. |
Abdel Aziz Taha; Leonhard Hennig; Petr Knoth; | arxiv-cs.CL | 2022-10-14 |
354 | Iterative Document-level Information Extraction Via Imitation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i.e., N-tuples representing a mapping from named slots to spans of text) within a document. |
YUNMO CHEN et. al. | arxiv-cs.CL | 2022-10-12 |
355 | Improving Continual Relation Extraction Through Prototypical Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction framework with Contrastive Learning, namely CRECL, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE. |
Chengwei Hu; Deqing Yang; Haoliang Jin; Zhen Chen; Yanghua Xiao; | arxiv-cs.IR | 2022-10-10 |
356 | Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. |
HAOYU WANG et. al. | arxiv-cs.CL | 2022-10-10 |
357 | ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose $\textbf{ConstGCN}$, a novel graph convolutional network which performs knowledge-based information propagation between entities along with all specific relation spaces without any prior graph construction. |
JI QI et. al. | arxiv-cs.CL | 2022-10-08 |
358 | Putting Them Under Microscope: A Fine-Grained Approach for Detecting Redundant Test Cases in Natural Language Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Following that, we propose Tscope, a fine-grained approach for redundant NL test case detection by dissecting test cases into atomic test tuple(s) with the entities restricted by associated relations. |
Zhiyuan Chang; Mingyang Li; Junjie Wang; Qing Wang; Shoubin Li; | arxiv-cs.SE | 2022-10-04 |
359 | TERMinator: A System for Scientific Texts Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction. |
Elena Bruches; Olga Tikhobaeva; Yana Dementyeva; Tatiana Batura; | arxiv-cs.CL | 2022-09-29 |
360 | A Unified Generative Framework Based on Prompt Learning for Various Information Extraction Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel composable prompt-based generative framework, which could be applied to a wide range of tasks in the field of Information Extraction. |
ZHIGANG KAN et. al. | arxiv-cs.IR | 2022-09-23 |
361 | Generalizing Through Forgetting — Domain Generalization for Symptom Event Extraction in Clinical Notes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present domain generalization for symptom extraction using pretraining and fine-tuning data that differs from the target domain in terms of institution and/or specialty and patient population. |
Sitong Zhou; Kevin Lybarger; Meliha Yetisgen; Mari Ostendorf; | arxiv-cs.CL | 2022-09-20 |
362 | Automatic Error Analysis for Document-level Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We build on the work of Kummerfeld and Klein (2013) to propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. |
ALIVA DAS et. al. | arxiv-cs.CL | 2022-09-15 |
363 | Deep Learning Models for Spatial Relation Extraction in Text Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Spatial relation extraction is the process of identifying geographic entities from text and determining their corresponding spatial relations. Traditional spatial relation … |
Kehan Wu; Xueying Zhang; Yulong Dang; Peng Ye; | Geo-spatial Information Science | 2022-09-07 |
364 | STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a simple yet effective self-training approach, named as STAD, for low-resource relation extraction. |
Junjie Yu; Xing Wang; Jiangjiang Zhao; Chunjie Yang; Wenliang Chen; | arxiv-cs.CL | 2022-09-03 |
365 | Less Is More: Rethinking State-of-the-art Continual Relation Extraction Models with A Frustratingly Easy But Effective Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Frustratingly easy but Effective Approach (FEA) method with two learning stages for CRE: 1) Fast Adaption (FA) warms up the model with only new data. |
PEIYI WANG et. al. | arxiv-cs.CL | 2022-09-01 |
366 | Multi-Scale Contrastive Knowledge Co-Distillation for Event Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present MulCo: Multi-Scale Contrastive Knowledge Co-Distillation, a fusion approach that shares knowledge across multiple event pair proximity bands in order to improve performance on all types of temporal datasets. |
Hao-Ren Yao; Luke Breitfeller; Aakanksha Naik; Chunxiao Zhou; Carolyn Rose; | arxiv-cs.CL | 2022-09-01 |
367 | Virtual Prompt Pre-training for Prototype-based Few-shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
KAI HE et. al. | Expert Syst. Appl. | 2022-09-01 |
368 | Supporting Medical Relation Extraction Via Causality-Pruned Semantic Dependency Forest Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a method to jointly model semantic and syntactic information from medical texts based on causal explanation theory. |
Yifan Jin; Jiangmeng Li; Zheng Lian; Chengbo Jiao; Xiaohui Hu; | arxiv-cs.CL | 2022-08-29 |
369 | GRASP: Guiding Model with RelAtional Semantics Using Prompt for Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To effectively exploit inherent knowledge of PLMs without extra layers and consider scattered semantic cues on the relation between the arguments, we propose a Guiding model with RelAtional Semantics using Prompt (GRASP). |
Junyoung Son; Jinsung Kim; Jungwoo Lim; Heuiseok Lim; | arxiv-cs.CL | 2022-08-26 |
370 | UniCausal: Unified Benchmark and Repository for Causal Text Mining Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, few datasets include cause-effect span annotations, which are needed for end-to-end causal relation extraction. To address these issues, we propose UniCausal, a unified benchmark for causal text mining across three tasks: (I) Causal Sequence Classification, (II) Cause-Effect Span Detection and (III) Causal Pair Classification. |
Fiona Anting Tan; Xinyu Zuo; See-Kiong Ng; | arxiv-cs.CL | 2022-08-19 |
371 | A Two-Phase Paradigm for Joint Entity-Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. |
BIN JI et. al. | arxiv-cs.CL | 2022-08-18 |
372 | A Sequence Tagging Based Framework for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the distance-based few-shot named entity recognition methods, we put forward the definition of the few-shot RE task based on the sequence tagging joint extraction approaches, and propose a few-shot RE framework for the task. |
Xukun Luo; Ping Wang; | arxiv-cs.CL | 2022-08-16 |
373 | A Hybrid Model of Classification and Generation for Spatial Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information. To address the above issue, we first view spatial relation extraction as a generation task and propose a novel hybrid model HMCGR for this task. |
Feng Wang Peifeng Li; Qiaoming Zhu; | arxiv-cs.CL | 2022-08-14 |
374 | SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose SagDRE model, which further considers and captures the original sequential information from the text. |
Ying Wei; Qi Li; | kdd | 2022-08-12 |
375 | Transfer Learning for Low-Resource Multilingual Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification (sometimes called relation extraction) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is … |
Arijit Nag; Bidisha Samanta; Animesh Mukherjee; Niloy Ganguly; Soumen Chakrabarti; | ACM Transactions on Asian and Low-Resource Language … | 2022-08-08 |
376 | KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present KPI-BERT, a system which employs novel methods of named entity recognition (NER) and relation extraction (RE) to extract and link key performance indicators (KPIs), e.g. revenue or interest expenses, of companies from real-world German financial documents. |
LARS HILLEBRAND et. al. | arxiv-cs.CL | 2022-08-03 |
377 | Joint Learning-based Causal Relation Extraction from Biomedical Literature Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. |
DONGLING LI et. al. | arxiv-cs.CL | 2022-08-02 |
378 | A Two-channel Model for Relation Extraction Using Multiple Trained Word Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yinmiao Wang; Zhimin Han; Keyou You; Zhiyun Lin; | Knowl. Based Syst. | 2022-08-01 |
379 | A Deep Learning Based Method Benefiting from Characteristics of Patents for Semantic Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
LIANG CHEN et. al. | J. Informetrics | 2022-08-01 |
380 | A Novel Chinese Relation Extraction Method Using Polysemy Rethinking Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qihui Zhao; Tianhan Gao; Nan Guo; | Applied Intelligence | 2022-07-29 |
381 | Enhancing Document-level Relation Extraction By Entity Knowledge Injection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. |
Xinyi Wang; Zitao Wang; Weijian Sun; Wei Hu; | arxiv-cs.CL | 2022-07-23 |
382 | Document-Level Relation Extraction with Structure Enhanced Transformer Encoder Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction aims at discovering relational facts among entity pairs in a document, which has attracted more and more attention in recent years. Most … |
Wanlong Liu; Li Zhou; DingYi Zeng; Hong Qu; | 2022 International Joint Conference on Neural Networks … | 2022-07-18 |
383 | An Overview of Distant Supervision for Relation Extraction with A Focus on Denoising and Pre-training Methods Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Extraction (RE) is a foundational task of natural language processing. RE seeks to transform raw, unstructured text into structured knowledge by identifying relational … |
William Hogan; | arxiv-cs.CL | 2022-07-17 |
384 | Multi-perspective Context Aggregation for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiaoyao Ding; Gang Zhou; Taojie Zhu; | Applied Intelligence | 2022-07-12 |
385 | Biographical Semi-Supervised Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. |
Alistair Plum; Tharindu Ranasinghe; Spencer Jones; Constantin Orasan; Ruslan Mitkov; | sigir | 2022-07-12 |
386 | SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In contrast, we propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE. |
Yuxin Xiao; Zecheng Zhang; Yuning Mao; Carl Yang; Jiawei Han; | naacl | 2022-07-09 |
387 | Few-Shot Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. |
Nicholas Popovic; Michael F?rber; | naacl | 2022-07-09 |
388 | Generic and Trend-aware Curriculum Learning for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a generic and trend-aware curriculum learning approach that effectively integrates textual and structural information in text graphs for relation extraction between entities, which we consider as node pairs in graphs. |
Nidhi Vakil; Hadi Amiri; | naacl | 2022-07-09 |
389 | Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the CoRE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information. |
YIWEI WANG et. al. | naacl | 2022-07-09 |
390 | Joint Extraction of Entities, Relations, and Events Via Modeling Inter-Instance and Inter-Label Dependencies IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous JointIE models often assume heuristic manually-designed dependency between the task instances and mean-field factorization for the joint distribution of instance labels, thus unable to capture optimal dependencies among instances and labels to improve representation learning and IE performance. To overcome these limitations, we propose to induce a dependency graph among task instances from data to boost representation learning. |
Minh Van Nguyen; Bonan Min; Franck Dernoncourt; Thien Nguyen; | naacl | 2022-07-09 |
391 | A Dataset for N-ary Relation Extraction of Drug Combinations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature. |
ARYEH TIKTINSKY et. al. | naacl | 2022-07-09 |
392 | Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Especially, we address the two-way interaction between COREF and RE that has not been the focus by previous work, and propose to introduce explicit interaction namely Graph Compatibility (GC) that is specifically designed to leverage task characteristics, bridging decisions of two tasks for direct task interference. |
Liyan Xu; Jinho Choi; | naacl | 2022-07-09 |
393 | Modeling Multi-Granularity Hierarchical Features for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel method to extract multi-granularity features based solely on the original input sentences. |
Xinnian Liang; Shuangzhi Wu; Mu Li; Zhoujun Li; | naacl | 2022-07-09 |
394 | Relation-Specific Attentions Over Entity Mentions for Enhanced Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a result, the distinct semantics between the different mentions of an entity are overlooked. To address this problem, we propose RSMAN in this paper which performs selective attentions over different entity mentions with respect to candidate relations. |
Jiaxin Yu; Deqing Yang; Shuyu Tian; | naacl | 2022-07-09 |
395 | HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning. |
SHULIANG LIU et. al. | naacl | 2022-07-09 |
396 | Document-Level Relation Extraction with Sentences Importance Estimation and Focusing IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a Sentence Importance Estimation and Focusing (SIEF) framework for DocRE, where we design a sentence importance score and a sentence focusing loss, encouraging DocRE models to focus on evidence sentences. |
Wang Xu; Kehai Chen; Lili Mou; Tiejun Zhao; | naacl | 2022-07-09 |
397 | EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples. |
BENFENG XU et. al. | naacl | 2022-07-09 |
398 | ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we introduce ViRel, a method for unsupervised discovery and learning of Visual Relations with graph-level analogy. |
Daniel Zeng; Tailin Wu; Jure Leskovec; | arxiv-cs.CV | 2022-07-04 |
399 | None Class Ranking Loss for Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This ignores the context of entity pairs and the label correlations between the none class and pre-defined classes, leading to sub-optimal predictions. To address this problem, we propose a new multi-label loss that encourages large margins of label confidence scores between each pre-defined class and the none class, which enables captured label correlations and context-dependent thresholding for label prediction. |
Yang Zhou; Wee Sun Lee; | ijcai | 2022-07-01 |
400 | Document-level Relation Extraction Via Subgraph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel subgraph reasoning (SGR) framework for document-level relation extraction. |
Xingyu Peng; Chong Zhang; Ke Xu; | ijcai | 2022-07-01 |
401 | Prototypical Networks Relation Classification Model Based on Entity Convolution Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jianzhou Feng; Qikai Wei; Jinman Cui; | Comput. Speech Lang. | 2022-07-01 |
402 | Position-aware Joint Entity and Relation Extraction with Attention Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we propose a joint entity and relation extraction model with an attention mechanism and position-attentive markers. |
Chenglong Zhang; Shuyong Gao; Haofen Wang; Wenqiang Zhang; | ijcai | 2022-07-01 |
403 | Function-words Adaptively Enhanced Attention Networks for Few-Shot Inverse Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a function words adaptively enhanced attention framework (FAEA) for few-shot inverse relation classification, in which a hybrid attention model is designed to attend class-related function words based on meta-learning. |
Chunliu Dou; Shaojuan Wu; Xiaowang Zhang; Zhiyong Feng; Kewen Wang; | ijcai | 2022-07-01 |
404 | Interactive Information Extraction By Semantic Information Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the shortages, we propose an Interactive Information Extraction (InterIE) model based on a novel Semantic Information Graph (SIG). |
SIQI FAN et. al. | ijcai | 2022-07-01 |
405 | FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The main efficiency bottleneck is that these methods use a Transformer-based pre-trained language model for encoding, which heavily affects the training speed and inference speed. To address this issue, we propose a fast relation extraction model (FastRE) based on convolutional encoder and improved cascade binary tagging framework. |
Guozheng Li; Xu Chen; Peng Wang; Jiafeng Xie; Qiqing Luo; | ijcai | 2022-07-01 |
406 | Global Inference with Explicit Syntactic and Discourse Structures for Dialogue-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate a novel dialogue-level mixed dependency graph (D2G) and an argument reasoning graph (ARG) for DiaRE with a global relation reasoning mechanism. |
HAO FEI et. al. | ijcai | 2022-07-01 |
407 | Contextualization and Generalization in Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this thesis, we study the behaviour of state-of-the-art models regarding generalization to facts unseen during training in two important Information Extraction tasks: Named Entity Recognition (NER) and Relation Extraction (RE). |
Bruno Taillé; | arxiv-cs.CL | 2022-06-15 |
408 | A Machine Learning Approach to Extracting Spatial Information from Geological Texts in Chinese Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Texts have become an important spatial data resource. Interpretation of unstructured geoscience texts using natural language processing methods can effectively facilitate the … |
DEPING CHU et. al. | International Journal of Geographical Information Science | 2022-06-15 |
409 | REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we observe two shortcomings of previous methods: first, there is no unified framework that works well under various relation extraction settings; second, effectively utilizing external knowledge as background information is absent. In this work, we propose a knowledge-enhanced generative model to mitigate these two issues. |
Sheng Zhang; Patrick Ng; Zhiguo Wang; Bing Xiang; | arxiv-cs.CL | 2022-06-10 |
410 | RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper shows that modeling an effective message-passing flow through an attention mechanism can be critical to tackling the compositionality and long-tail challenges in VRR. |
Jun Chen; Aniket Agarwal; Sherif Abdelkarim; Deyao Zhu; Mohamed Elhoseiny; | cvpr | 2022-06-07 |
411 | VRDFormer: End-to-End Video Visual Relation Detection With Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most previous works adopt a multi-stage framework for video visual relation detection (VidVRD), which cannot capture long-term spatiotemporal contexts in different stages and also suffers from inefficiency. In this paper, we propose a transformerbased framework called VRDFormer to unify these decoupling stages. |
Sipeng Zheng; Shizhe Chen; Qin Jin; | cvpr | 2022-06-07 |
412 | A Chinese Multi-modal Relation Extraction Model for Internet Security of Finance Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As the base of the whole economy and society, internet security of finance directly affects the overall development of the country. With the development of the Internet, it is … |
Qinghan Lai; Shuai Ding; J. Gong; Jin’an Cui; Song Liu; | 2022 52nd Annual IEEE/IFIP International Conference on … | 2022-06-01 |
413 | MORE: A Metric Learning Based Framework for Open-domain Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Most existing OpenRE methods either do not fully benefit from high-quality labeled corpora or can not learn semantic representation directly, affecting downstream clustering efficiency. To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction). |
Yutong Wang; Renze Lou; Kai Zhang; MaoYan Chen; Yujiu Yang; | arxiv-cs.CL | 2022-06-01 |
414 | Dual-Channel and Hierarchical Graph Convolutional Networks for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
QI SUN et. al. | Expert Syst. Appl. | 2022-06-01 |
415 | Revisiting DocRED — Addressing The False Negative Problem in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset. |
Qingyu Tan; Lu Xu; Lidong Bing; Hwee Tou Ng; Sharifah Mahani Aljunied; | arxiv-cs.CL | 2022-05-25 |
416 | TAGPRIME: A Unified Framework for Relational Structure Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent works usually propose sophisticated models for each task independently and pay less attention to the commonality of these tasks and to have a unified framework for all the tasks. In this work, we propose to take a unified view of all these tasks and introduce TAGPRIME to address relational structure extraction problems. |
I-HUNG HSU et. al. | arxiv-cs.CL | 2022-05-25 |
417 | An Embarrassingly Simple Model for Dialogue Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple yet effective model named SimpleRE for the RE task. |
F. Xue; A. Sun; H. Zhang; J. Ni; E. -S. Chng; | icassp | 2022-05-22 |
418 | A Knowledge/Data Enhanced Method for Joint Event and Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate those issues, we propose a Knowledge/Data Enhanced method for Event and TempRel Extraction, which integrates the temporal commonsense knowledge, data augmentation and Focal Loss function into one single extraction system. |
X. Zhang; L. Zang; P. Cheng; Y. Wang; S. Hu; | icassp | 2022-05-22 |
419 | Improving Long Tailed Document-Level Relation Extraction Via Easy Relation Augmentation and Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the long-tailed distribution problem, we propose an Easy Relation Augmentation(ERA) method for improving DocRE by enhancing the performance of tailed relations. |
YANGKAI DU et. al. | arxiv-cs.CL | 2022-05-21 |
420 | DeepStruct: Pretraining of Language Models for Structure Prediction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a method for improving the structural understanding abilities of language models. |
CHENGUANG WANG et. al. | arxiv-cs.CL | 2022-05-20 |
421 | Why Only Micro-F1? Class Weighting of Measures for Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. |
David Harbecke; Yuxuan Chen; Leonhard Hennig; Christoph Alt; | arxiv-cs.CL | 2022-05-19 |
422 | Summarization As Indirect Supervision for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present SuRE, which converts RE into a summarization formulation. |
Keming Lu; I-Hung Hsu; Wenxuan Zhou; Mingyu Derek Ma; Muhao Chen; | arxiv-cs.CL | 2022-05-19 |
423 | A Simple Yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, this paper proposes a direct addition approach to introduce relation information. |
Yang Liu; Jinpeng Hu; Xiang Wan; Tsung-Hui Chang; | arxiv-cs.CL | 2022-05-19 |
424 | Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a weighted contrastive learning method by leveraging the supervised data to estimate the reliability of pre-training instances and explicitly reduce the effect of noise. |
ZHEN WAN et. al. | arxiv-cs.CL | 2022-05-18 |
425 | Text-to-Table: A New Way of Information Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we formalize text-to-table as a sequence-to-sequence (seq2seq) problem. |
Xueqing Wu; Jiacheng Zhang; Hang Li; | acl | 2022-05-17 |
426 | PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In response, we explore a simple baseline approach (PARE) in which all sentences of a bag are concatenated into a passage of sentences, and encoded jointly using BERT. |
Vipul Rathore; Kartikeya Badola; Parag Singla; Mausam .; | acl | 2022-05-17 |
427 | DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our goal is to study the novel task of distant supervision for multilingual relation extraction (Multi DS-RE). |
Abhyuday Bhartiya; Kartikeya Badola; Mausam .; | acl | 2022-05-17 |
428 | Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, we observe that the models trained on DocRED have low recall on our relabeled dataset and inherit the same bias in the training data. Through the analysis of annotators’ behaviors, we figure out the underlying reason for the problems above: the scheme actually discourages annotators from supplementing adequate instances in the revision phase. |
QUZHE HUANG et. al. | acl | 2022-05-17 |
429 | Packed Levitated Marker for Entity and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel span representation approach, named Packed Levitated Markers (PL-Marker), to consider the interrelation between the spans (pairs) by strategically packing the markers in the encoder. |
Deming Ye; Yankai Lin; Peng Li; Maosong Sun; | acl | 2022-05-17 |
430 | Pre-training to Match for Unified Low-shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Multi-Choice Matching Networks to unify low-shot relation extraction. |
Fangchao Liu; Hongyu Lin; Xianpei Han; Boxi Cao; Le Sun; | acl | 2022-05-17 |
431 | Event-Event Relation Extraction Using Probabilistic Box Embedding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints. |
EUNJEONG HWANG et. al. | acl | 2022-05-17 |
432 | Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a generic and trend-aware curriculum learning approach for graph neural networks. |
Nidhi Vakil; Hadi Amiri; | arxiv-cs.CL | 2022-05-17 |
433 | Pre-trained Language Models As Re-Annotators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We proposed a novel credibility score to reveal the likelihood of annotation inconsistencies based on the neighbouring consistency. |
Chang Shu; | arxiv-cs.CL | 2022-05-11 |
434 | GRAPHCACHE: Message Passing As Caching for Sentence-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the GRAPHCACHE (Graph Neural Network as Caching) module, that propagates the features across sentences to learn better representations for RE. |
YIWEI WANG et. al. | arxiv-cs.CL | 2022-05-08 |
435 | Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing approaches for MNER and MRE usually suffer from error sensitivity when irrelevant object images incorporated in texts. To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance. |
XIANG CHEN et. al. | arxiv-cs.CL | 2022-05-06 |
436 | Relation Extraction As Open-book Examination: Retrieval-enhanced Prompt Tuning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. |
XIANG CHEN et. al. | arxiv-cs.CL | 2022-05-04 |
437 | HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning. |
XUMING HU et. al. | arxiv-cs.CL | 2022-05-04 |
438 | Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Especially, we address the two-way interaction between COREF and RE that has not been the focus by previous work, and propose to introduce explicit interaction namely Graph Compatibility (GC) that is specifically designed to leverage task characteristics, bridging decisions of two tasks for direct task interference. |
Liyan Xu; Jinho D. Choi; | arxiv-cs.CL | 2022-05-04 |
439 | Few-Shot Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. |
Nicholas Popovic; Michael Färber; | arxiv-cs.CL | 2022-05-04 |
440 | Biographical: A Semi-Supervised Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. |
Alistair Plum; Tharindu Ranasinghe; Spencer Jones; Constantin Orasan; Ruslan Mitkov; | arxiv-cs.IR | 2022-05-02 |
441 | An Adaptable, High-performance Relation Extraction System for Complex Sentences Related Papers Related Patents Related Grants Related Venues Related Experts View |
Anu Thomas; Sangeetha Sivanesan; | Knowl. Based Syst. | 2022-05-01 |
442 | Knowledge Guided Distance Supervision for Biomedical Relation Extraction in Chinese Electronic Medical Records IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qing Zhao; Dezhong Xu; Jianqiang Li; Linna Zhao; Faheem Akhtar Rajput; | Expert Syst. Appl. | 2022-05-01 |
443 | Heterogenous Affinity Graph Inference Network for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Rongzhen Li; Zhong Jiang; Zhongxuan Xue; Qizhu Dai; Xue Li; | Knowl. Based Syst. | 2022-05-01 |
444 | What Do You Mean By Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: At the same time, setup clarity has decreased, contributing to increased difficulty of reliable empirical evaluation (Taill\’e et al., 2020). In this paper, we provide a comprehensive survey of RE datasets, and revisit the task definition and its adoption by the community. |
Elisa Bassignana; Barbara Plank; | arxiv-cs.CL | 2022-04-28 |
445 | CREER: A Large-Scale Corpus for Relation Extraction and Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We describe the design and use of the CREER dataset, a large corpus annotated with rich English grammar and semantic attributes. |
Yu-Siou Tang; Chung-Hsien Wu; | arxiv-cs.CL | 2022-04-27 |
446 | Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a function words adaptively enhanced attention framework (FAEA) for few-shot inverse relation classification, in which a hybrid attention model is designed to attend class-related function words based on meta-learning. |
Chunliu Dou; Shaojuan Wu; Xiaowang Zhang; Zhiyong Feng; Kewen Wang; | arxiv-cs.CL | 2022-04-26 |
447 | Graph Convolutional Networks for Chemical Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting information regarding novel chemicals and chemical reactions from chemical patents plays a vital role in the chemical and pharmaceutical industry. Due to the increasing … |
D. Mahendran; Christina Tang; Bridget Mcinnes; | Companion Proceedings of the Web Conference 2022 | 2022-04-25 |
448 | Knowledge Distillation for Discourse Relation Analysis Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Automatically identifying the discourse relations can help many downstream NLP tasks such as reading comprehension. It can be categorized into explicit and implicit discourse … |
Congcong Jiang; T. Qian; Bing Liu; | Companion Proceedings of the Web Conference 2022 | 2022-04-25 |
449 | It Takes Two Flints to Make A Fire: Multitask Learning of Neural Relation and Explanation Classifiers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. |
Zheng Tang; Mihai Surdeanu; | arxiv-cs.CL | 2022-04-24 |
450 | Enhanced Graph Convolutional Network Based on Node Importance for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
QI SUN et. al. | Neural Computing and Applications | 2022-04-22 |
451 | Decorate The Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a simple yet effective method to systematically generate comprehensive prompts that reformulate the relation extraction task as a cloze-test task under a simple prompt formulation. |
Hui-Syuan Yeh; Thomas Lavergne; Pierre Zweigenbaum; | arxiv-cs.CL | 2022-04-21 |
452 | Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In a corpus analysis we find that over 80 % of documents contain relevant entities. |
Amelie Wührl; Roman Klinger; | arxiv-cs.CL | 2022-04-21 |
453 | A Masked Image Reconstruction Network for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Document-level Relation Extraction model based on a Masked Image Reconstruction network (DRE-MIR), which models inference as a masked image reconstruction problem to capture the correlations between relationships. |
Liang Zhang; Yidong Cheng; | arxiv-cs.CL | 2022-04-20 |
454 | FREDA: Flexible Relation Extraction Data Annotation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We are proposing an approach in order to produce high-quality datasets for the task of Relation Extraction quickly. |
Michael Strobl; Amine Trabelsi; Osmar Zaiane; | arxiv-cs.CL | 2022-04-14 |
455 | A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label document-level biomedical relation extraction models. |
Dongxu Zhang; Sunil Mohan; Michaela Torkar; Andrew McCallum; | arxiv-cs.CL | 2022-04-13 |
456 | MedDistant19: Towards An Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, we noticed several inconsistencies in the data construction process of these benchmarks, and where there is no train-test leakage, the focus is on interactions between narrower entity types. This work presents a more accurate benchmark MedDistant19 for broad-coverage distantly supervised biomedical relation extraction that addresses these shortcomings and is obtained by aligning the MEDLINE abstracts with the widely used SNOMED Clinical Terms knowledge base. |
Saadullah Amin; Pasquale Minervini; David Chang; Pontus Stenetorp; Günter Neumann; | arxiv-cs.CL | 2022-04-10 |
457 | BioRED: A Rich Biomedical Relation Extraction Dataset IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including BERT-based models, on the NER and RE tasks. |
Ling Luo; Po-Ting Lai; Chih-Hsuan Wei; Cecilia N Arighi; Zhiyong Lu; | arxiv-cs.CL | 2022-04-08 |
458 | A Sequence-to-sequence Approach for Document-level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components. |
John Giorgi; Gary D. Bader; Bo Wang; | arxiv-cs.CL | 2022-04-03 |
459 | Enhance Prototypical Networks with Hybrid Attention and Confusing Loss Function for Few-shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
YIBING LI et. al. | Neurocomputing | 2022-04-01 |
460 | A Pattern-first Pipeline Approach for Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zheng Chen; Changyu Guo; | Neurocomputing | 2022-04-01 |
461 | NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their great successes, these graph-based methods, which normally only consider the words within the mentions in the process of building graphs and reasoning, tend to ignore the non-entity clue words that are not in the mentions but provide important clue information for relation reasoning. To alleviate this problem, we treat graph-based document-level RE models as an encoder-decoder framework, which typically uses a pre-trained language model as the encoder and a GNN model as the decoder, and propose a novel graph-based model NC-DRE that introduces decoder-to-encoder attention mechanism to leverage Non-entity Clue information for Document-level Relation Extraction. |
Liang Zhang; Yidong Cheng; | arxiv-cs.CL | 2022-04-01 |
462 | Evaluation of Semantic Relations Impact in Query Expansion-based Retrieval Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These techniques are aimed at estimating the goal of an input query reformulating it as an intent, commonly relying on textual resources built exploiting different semantic relations like \emph{synonymy}, \emph{antonymy} and many others. The aim of this paper is to generate such resources using the labels of a given taxonomy as source of information. |
Lorenzo Massai; | arxiv-cs.CL | 2022-03-30 |
463 | Improving Persian Relation Extraction Models By Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present our augmented dataset and the results and findings of our system, participated in the Persian relation Extraction shared task of NSURL 2021 workshop. |
Moein Salimi Sartakhti; Romina Etezadi; Mehrnoush Shamsfard; | arxiv-cs.CL | 2022-03-29 |
464 | Construction of A Chinese Corpus for Multi-Type Economic Event Relation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We construct a Chinese Economic Event Treebank (CEETB), focusing on revealing economic and finance events and their relations. Investigating economic event relations will benefit … |
QI-ZHI WAN et. al. | Transactions on Asian and Low-Resource Language Information … | 2022-03-26 |
465 | A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel model, called Densely Connected Criss-Cross Attention Network (Dense-CCNet), for document-level RE, which can complete logical reasoning at the entity-pair-level. |
Liang Zhang; Yidong Cheng; | arxiv-cs.CL | 2022-03-25 |
466 | Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a semi-supervised framework for DocRE with three novel components. |
Qingyu Tan; Ruidan He; Lidong Bing; Hwee Tou Ng; | arxiv-cs.CL | 2022-03-21 |
467 | Learning to Reason Deductively: Math Word Problem Solving As Complex Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we view the task as a complex relation extraction problem, proposing a novel approach that presents explainable deductive reasoning steps to iteratively construct target expressions, where each step involves a primitive operation over two quantities defining their relation. |
Zhanming Jie; Jierui Li; Wei Lu; | arxiv-cs.CL | 2022-03-19 |
468 | BIOS: An Algorithmically Generated Biomedical Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce the Biomedical Informatics Ontology System (BIOS), the first large-scale publicly available BioMedKG generated completely by machine learning algorithms. |
SHENG YU et. al. | arxiv-cs.CL | 2022-03-18 |
469 | DPNet: Domain-aware Prototypical Network for Interdisciplinary Few-shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
BO LV et. al. | Applied Intelligence | 2022-03-18 |
470 | RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods. |
Yew Ken Chia; Lidong Bing; Soujanya Poria; Luo Si; | arxiv-cs.CL | 2022-03-17 |
471 | Multimodal Learning on Graphs for Disease Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Methods: We introduce REMAP, a multimodal approach for disease relation extraction and classification. |
Yucong Lin; Keming Lu; Sheng Yu; Tianxi Cai; Marinka Zitnik; | arxiv-cs.LG | 2022-03-16 |
472 | Cross-lingual Inference with A Chinese Entailment Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the first pipeline for building Chinese entailment graphs, which involves a novel high-recall open relation extraction (ORE) method and the first Chinese fine-grained entity typing dataset under the FIGER type ontology. |
Tianyi Li; Sabine Weber; Mohammad Javad Hosseini; Liane Guillou; Mark Steedman; | arxiv-cs.CL | 2022-03-11 |
473 | AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First – Using Relation Extraction to Identify Entities Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking … |
Nicholas Popovic; Walter Laurito; Michael Färber; | International Workshop on Semantic Evaluation | 2022-03-10 |
474 | OneRel:Joint Entity and Relation Extraction with One Module in One Step Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem. |
Yu-Ming Shang; Heyan Huang; Xian-Ling Mao; | arxiv-cs.CL | 2022-03-10 |
475 | AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First — Using Relation Extraction to Identify Entities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. |
Nicholas Popovic; Walter Laurito; Michael Färber; | arxiv-cs.CL | 2022-03-10 |
476 | A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study presents an engineering framework of medical entity recognition, relation extraction and attribute extraction, which are unified in annotation, modeling and evaluation. |
Enwei Zhu; Qilin Sheng; Huanwan Yang; Jinpeng Li; | arxiv-cs.CL | 2022-03-07 |
477 | Consistent Representation Learning for Continual Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these memory-based methods tend to overfit the memory samples and perform poorly on imbalanced datasets. To solve these challenges, a consistent representation learning method is proposed, which maintains the stability of the relation embedding by adopting contrastive learning and knowledge distillation when replaying memory. |
Kang Zhao; Hua Xu; Jiangong Yang; Kai Gao; | arxiv-cs.CL | 2022-03-05 |
478 | Bi-GRU Relation Extraction Model Based on Keywords Attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relational extraction plays an important role in the field of natural language processing to predict semantic relationships between entities in a sentence. Currently, most models … |
YUANYUAN ZHANG et. al. | Data Intelligence | 2022-03-02 |
479 | Novel Target Attention Convolutional Neural Network for Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhiqiang Geng; Jun Li; Yongming Han; Yanhui Zhang; | Inf. Sci. | 2022-03-01 |
480 | EPPAC: Entity Pre-typing Relation Classification with Prompt AnswerCentralizing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address these issues, a novel paradigm, Entity Pre-typing Relation Classification with Prompt Answer Centralizing(EPPAC) is proposed in this paper. |
Jiejun Tan; Wenbin Hu; WeiWei Liu; | arxiv-cs.CL | 2022-02-28 |
481 | HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a hierarchical contrastive learning Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction. |
Dongyang Li; Taolin Zhang; Nan Hu; Chengyu Wang; Xiaofeng He; | arxiv-cs.CL | 2022-02-27 |
482 | A Generative Model for Relation Extraction and Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel generative model for relation extraction and classification (which we call GREC), where RE is modeled as a sequence-to-sequence generation task. |
Jian Ni; Gaetano Rossiello; Alfio Gliozzo; Radu Florian; | arxiv-cs.CL | 2022-02-26 |
483 | CorefDRE: Document-level Relation Extraction with Coreference Resolution Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction is to extract relation facts from a document consisting of multiple sentences, in which pronoun crossed sentences are a ubiquitous phenomenon … |
Zhongxuan Xue; Rongzhen Li; Qizhu Dai; Zhong Jiang; | arxiv-cs.CL | 2022-02-22 |
484 | Automatically Generating Counterfactuals for Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the problem of automatically generating CAD for RC tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. |
Mi Zhang; Tieyun Qian; Ting Zhang; | arxiv-cs.CL | 2022-02-21 |
485 | Towards Effective Multi-Task Interaction for Entity-Relation Extraction: A Unified Framework with Selection Recurrent Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by this, we propose a novel and unified cascade framework that combines the advantages of both sequential information propagation and implicit interaction. |
An Wang; Ao Liu; Hieu Hanh Le; Haruo Yokota; | arxiv-cs.CL | 2022-02-15 |
486 | A Novel Entity Joint Annotation Relation Extraction Model Related Papers Related Patents Related Grants Related Venues Related Experts View |
Meng Xu; D. Pi; Jianjun Cao; Shuilian Yuan; | Applied Intelligence | 2022-02-12 |
487 | Active Learning on Pre-trained Language Model with Task-Independent Triplet Loss Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a task-independent batch acquisition method using triplet loss to distinguish hard samples in an unlabeled data pool with similar features but difficult to identify labels. |
Seungmin Seo; Donghyun Kim; Youbin Ahn; Kyong-Ho Lee; | aaai | 2022-02-07 |
488 | OneRel: Joint Entity and Relation Extraction with One Module in One Step IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, previous joint methods suffer from the problems of cascading errors and redundant information. To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem. |
Yu-Ming Shang; Heyan Huang; Xianling Mao; | aaai | 2022-02-07 |
489 | Unified Named Entity Recognition As Word-Word Relation Classification IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present a novel alternative by modeling the unified NER as word-word relation classification, namely W^2NER. |
JINGYE LI et. al. | aaai | 2022-02-07 |
490 | Selecting Optimal Context Sentences for Event-Event Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a novel method to better model document-level context with important context sentences for event-event relation extraction. |
Hieu Man; Nghia Trung Ngo; Linh Ngo Van; Thien Huu Nguyen; | aaai | 2022-02-07 |
491 | Entity and Relation Collaborative Extraction Approach Based on Multi-head Attention and Gated Mechanism Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity and relation extraction has been widely studied in natural language processing, and some joint methods have been proposed in recent years. However, existing studies still … |
Wei Zhao; Shan Zhao; Shuhui Chen; T. Weng; Wenjie Kang; | Connection Science | 2022-02-04 |
492 | A Deep Learning Relation Extraction Approach to Support A Biomedical Semi-automatic Curation Task: The Case of The Gluten Bibliome Related Papers Related Patents Related Grants Related Venues Related Experts View |
M. Pérez-Pérez; Tânia Ferreira; G. Igrejas; F. F. Riverola; | Expert Syst. Appl. | 2022-02-01 |
493 | A Survey of The Extraction and Applications of Causal Relations Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Causationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a … |
Brett Drury; Hugo Gonçalo Oliveira; Alneu de Andrade Lopes; | Natural Language Engineering | 2022-01-20 |
494 | A Entity Relation Extraction Model with Enhanced Position Attention in Food Domain Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qing-Bang Wang; Qingchuan Zhang; Min Zuo; Si-Yu He; Baoyu Zhang; | Neural Processing Letters | 2022-01-18 |
495 | Temporal Relation Extraction with A Graph-Based Deep Biaffine Attention Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel temporal information extraction model based on deep biaffine attention to extract temporal relationships between events in unstructured text efficiently and accurately. |
Bo-Ying Su; Shang-Ling Hsu; Kuan-Yin Lai; Amarnath Gupta; | arxiv-cs.CL | 2022-01-16 |
496 | Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose two novel techniques, Context Guided Mention Integration and Inter-pair Reasoning (CGM2IR), to improve the DRE. |
Chao Zhao; Daojian Zeng; Lu Xu; Jianhua Dai; | arxiv-cs.CL | 2022-01-13 |
497 | MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE), an important information extraction task, faced the great challenge brought by limited annotation data. To this end, distant supervision was proposed to … |
Rui Li; Cheng Yang; Tingwei Li; Sen Su; | ACM Transactions on Information Systems (TOIS) | 2022-01-12 |
498 | A Relation Aware Embedding Mechanism for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiang Li; Yuwei Li; Jun-an Yang; Hui Liu; Peng Hu; | Applied Intelligence | 2022-01-11 |
499 | DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population. |
NINGYU ZHANG et. al. | arxiv-cs.CL | 2022-01-10 |
500 | Scale–Relation Joint Decoupling Network for Remote Sensing Image Semantic Segmentation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As we all know, remote sensing (RS) images contain multiscale and numerous RS objects, along with massive and complex spatial topological relationships, such as the adjacency, … |
JIE NIE et. al. | IEEE Transactions on Geoscience and Remote Sensing | 2022-01-01 |
501 | Video Visual Relation Detection Via 3D Convolutional Neural Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video visual relation detection, which aims to detect the visual relations between objects in the form of relation triplet (e.g., “person-ride-bike”, “dog-toward-car”, etc.), is a … |
M. Qu; Jianxun Cui; Tonghua Su; Ganlin Deng; Wenkai Shao; | IEEE Access | 2022-01-01 |
502 | Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Few-shot Relation Extraction refers to fast adaptation to novel relation classes with few samples through training on the known relation classes. Most existing methods focus on … |
Yang Liu; Jinpeng Hu; Xiang Wan; Tsung-Hui Chang; | NAACL-HLT | 2022-01-01 |
503 | JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: This paper describes our system in the SemEval-2022 Task 12: ‘linking mathematical symbols to their descriptions’, achieving first on the leaderboard for all the subtasks … |
Sung-min Lee; Seung-Hoon Na; | International Workshop on Semantic Evaluation | 2022-01-01 |
504 | RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage. However, it still faces some challenges since the unseen relations … |
Shusen Wang; Bosen Zhang; Yajing Xu; Yanan Wu; Bo Xiao; | NAACL-HLT | 2022-01-01 |
505 | Revisiting DocRED – Addressing The Overlooked False Negative Problem in Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The DocRED dataset is one of the most popular and widely used benchmarks for document-level relation extraction (RE). It adopts a recommend-revise annotation scheme so as to have … |
Qingyu Tan; Lu Xu; Lidong Bing; H. Ng; | ArXiv | 2022-01-01 |
506 | Sparse and Time-Varying Predictive Relation Extraction for Root Cause Quantification of Nonstationary Process Faults Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Root cause diagnosis (RCD) is an important technique for maintaining process safety, which infers the causalities between faulty measurements to locate the root cause of the … |
Pengyu Song; Chunhui Zhao; Biao Huang; Min Wu; | IEEE Transactions on Instrumentation and Measurement | 2022-01-01 |
507 | Pretrained Knowledge Base Embeddings for Improved Sentential Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: In this work we put forward to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on the sentential Relation … |
Andrea Papaluca; D. Krefl; H. Suominen; Artem Lenskiy; | Annual Meeting of the Association for Computational … | 2022-01-01 |
508 | Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: This study introduces a novel approach to the joint extraction of entities and relations by stacking convolutional neural networks (CNNs) on pretrained language models. We adopt … |
Youmi Ma; Tatsuya Hiraoka; Naoaki Okazaki; | SPNLP | 2022-01-01 |
509 | Deep Neural Network-based Relation Extraction: An Overview IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge is a formal way of understanding the world, providing human-level cognition and intelligence for the next-generation artificial intelligence (AI). An effective way to … |
Hailin Wang; Ke Qin; Rufai Yusuf Zakari; Guisong Liu; Guoming Lu; | Neural Computing and Applications | 2022-01-01 |
510 | Balancing Precision and Recall for Neural Biomedical Event Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Biomedicalevent extraction is an essential task in the biomedical research. Existing models suffer from the issue of low recall due to the large proportion of unrecognized events … |
Fangfang Su; Yue Zhang; Fei Li; Donghong Ji; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2022-01-01 |
511 | Improving Relation Extraction Through Syntax-induced Pre-training with Dependency Masking IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation extraction (RE) is an important natural language processing task that predicts the relation between two given entities, where a good understanding of the contextual … |
Yuanhe Tian; Yan Song; Fei Xia; | FINDINGS | 2022-01-01 |
512 | ArabIE: Joint Entity, Relation and Event Extraction for Arabic Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Previous work on Arabic information extraction has mainly focused on named entity recognition and very little work has been done on Arabic relation extraction and event … |
Niama El Khbir; Nadi Tomeh; Thierry Charnois; | Workshop on Arabic Natural Language Processing | 2022-01-01 |
513 | Prompt-Based Prototypical Framework for Continual Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Continual relation extraction (CRE) is an important task of continual learning, which aims to learn incessantly emerging new relations between entities from texts. To avoid … |
Hang Zhang; Bin Liang; Min Yang; Hui Wang; Ruifeng Xu; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2022-01-01 |
514 | Task-Adaptive Feature Fusion for Generalized Few-Shot Relation Classification in An Open World Environment Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Classification (RC) is an important task in information extraction. In most real-world scenarios, the frequency of relations often follows a long-tailed and open-ended … |
XIAOFENG CHEN et. al. | IEEE/ACM Transactions on Audio, Speech, and Language … | 2022-01-01 |
515 | Exploit Feature and Relation Hierarchy for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical … |
Mi Zhang; T. Qian; Bing Liu; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2022-01-01 |
516 | A Two-Step Approach for Explainable Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hugo Ayats; Peggy Cellier; S. Ferré; | International Symposium on Intelligent Data Analysis | 2022-01-01 |
517 | Cross-lingual Transfer Learning for Relation Extraction Using Universal Dependencies Related Papers Related Patents Related Grants Related Venues Related Experts View |
Nasrin Taghizadeh; H. Faili; | Comput. Speech Lang. | 2022-01-01 |
518 | Temporal Relation Extraction in Clinical Texts IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient’s journey, including … |
YOHAN BONESCKI GUMIEL et. al. | ACM Computing Surveys (CSUR) | 2022-01-01 |
519 | Low-resource Extraction with Knowledge-aware Pairwise Prototype Learning IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge Extraction (KE) aims at extracting structured information from raw texts, such as relation extraction and event extraction. One of the major issues for KE is the … |
SHUMIN DENG et. al. | Knowl. Based Syst. | 2022-01-01 |
520 | Event Temporal Relation Extraction with Attention Mechanism and Graph Neural Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiaoliang Xu; Gao Tong; Wang Yuxiang; Xinle Xuan; | Tsinghua Science & Technology | 2022-01-01 |
521 | Improving Few-Shot Relation Classification By Prototypical Representation Learning with Definition Text Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhenzhen Li; Yuyang Zhang; Jian-Yun Nie; Dongsheng Li; | NAACL-HLT | 2022-01-01 |
522 | Chemical–protein Relation Extraction with Ensembles of Carefully Tuned Pretrained Language Models IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The identification of chemical–protein interactions described in the literature is an important task with applications in drug design, precision medicine and biotechnology. Manual … |
LEON WEBER et. al. | Database: The Journal of Biological Databases and Curation | 2022-01-01 |
523 | Enhancing Structure Modeling for Relation Extraction with Fine-grained Gating and Co-attention Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yubo Chen; Chuhan Wu; Yongfeng Huang; | Neurocomputing | 2022-01-01 |
524 | Few-Shot Relation Classification Research Based on Prototypical Network and Causal Intervention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: To improve the accuracy of the few-shot relation classification task, the research, which weakens the influence of confounder on the performance of the model and enhances the … |
Zhiming Li; Feifan Ouyang; Chunlong Zhou; Yihao He; Limin Shen; | IEEE Access | 2022-01-01 |
525 | Low Resource Causal Event Detection from Biomedical Literature Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recognizing causal precedence relations among the chemical interactions in biomedical literature is crucial to understanding the underlying biological mechanisms. However, … |
Zhengzhong Liang; Enrique Noriega-Atala; Clayton T. Morrison; M. Surdeanu; | Workshop on Biomedical Natural Language Processing | 2022-01-01 |
526 | Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper proposes novel drug-protein relation extraction models that indirectly utilize distant supervision data. Concretely, instead of adding distant supervision data to the … |
Naoki Iinuma; Makoto Miwa; Yutaka Sasaki; | Workshop on Biomedical Natural Language Processing | 2022-01-01 |
527 | Comparing Encoder-Only and Encoder-Decoder Transformers for Relation Extraction from Biomedical Texts: An Empirical Study on Ten Benchmark Datasets IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Biomedical relation extraction, aiming to automatically discover high-quality and semantic relations between the entities from free text, is becoming a vital step for automated … |
Mourad Sarrouti; Carson Tao; Y. M. Randriamihaja; | Workshop on Biomedical Natural Language Processing | 2022-01-01 |
528 | Extracting Entity Synonymous Relations Via Context-Aware Permutation Invariance Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Discovering entity synonymous relations is an important work for many entity-based applications. Existing entity synonymous relation extraction approaches are mainly based on … |
Nan Yan; Subin Huang; Chao Kong; | International Journal of Information Technology and Web … | 2022-01-01 |
529 | Multi-view Interaction Learning for Few-Shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: So this paper proposes a novel interactive attention network (IAN) which uses inter-instance and intra-instance interactive information to classify the relations. |
YI HAN et. al. | cikm | 2021-12-30 |
530 | Uncertainty-Aware Self-Training for Semi-Supervised Event Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the use of uncertainty-aware self-training framework (UAST) to quantify the model uncertainty for coping with pseudo-labeling errors. |
PENGFEI CAO et. al. | cikm | 2021-12-30 |
531 | SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Given the lack of reliable ground truth data, we present SoMeSci-Software Mentions in Science-a gold standard knowledge graph of software mentions in scientific articles. |
David Schindler; Felix Bensmann; Stefan Dietze; Frank Krüger; | cikm | 2021-12-30 |
532 | Zero-shot Relation Classification from Side Information IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a zero-shot learning relation classification (ZSLRC) framework that improves on state-of-the-art by its ability to recognize novel relations that were not present in training data. |
Jiaying Gong; Hoda Eldardiry; | cikm | 2021-12-30 |
533 | HORNET: Enriching Pre-trained Language Representations with Heterogeneous Knowledge Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel KEPLM named HORNET, which integrates Heterogeneous knowledge from various structured and unstructured sources into the Roberta NETwork and hence takes full advantage of both linguistic and factual knowledge simultaneously. |
TAOLIN ZHANG et. al. | cikm | 2021-12-30 |
534 | REFORM: Error-Aware Few-Shot Knowledge Graph Completion IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the aforementioned issues, in this paper, we study a novel problem of error-aware few-shot KG completion and present a principled KG completion framework REFORM. |
Song Wang; Xiao Huang; Chen Chen; Liang Wu; Jundong Li; | cikm | 2021-12-30 |
535 | Learning Discriminative and Unbiased Representations for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To learn more discriminative and unbiased representations for FSRE, this paper proposes a two-stage approach via supervised contrastive learning and sentence- and entity-level prototypical networks. |
Jiale Han; Bo Cheng; Guoshun Nan; | cikm | 2021-12-30 |
536 | WebKE: Knowledge Extraction from Semi-structured Web with Pre-trained Markup Language Model IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel multi-phase machine reading framework, called WebKE. |
Chenhao Xie; Wenhao Huang; Jiaqing Liang; Chengsong Huang; Yanghua Xiao; | cikm | 2021-12-30 |
537 | Spatially Oriented Convolutional Neural Network for Spatial Relation Extraction from Natural Language Texts IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Spatial relation extraction (e.g., topological relations, directional relations, and distance relations) from natural language descriptions is a fundamental but challenging task … |
Qinjun Qiu; Zhong Xie; K. Ma; Zhanlong Chen; Liufeng Tao; | Transactions in GIS | 2021-12-27 |
538 | Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to catch more annotation errors in the dataset while reannotating fewer instances. |
Akshay Parekh; Ashish Anand; Amit Awekar; | arxiv-cs.CL | 2021-12-26 |
539 | Deeper Clinical Document Understanding Using Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we propose a text mining framework comprising of Named Entity Recognition (NER) and Relation Extraction (RE) models, which expands on previous work in three main ways. |
Hasham Ul Haq; Veysel Kocaman; David Talby; | arxiv-cs.CL | 2021-12-25 |
540 | Neural Architectures for Biological Inter-Sentence Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, biological context is defined as the type of biological system within which the biochemical event is observed. |
Enrique Noriega-Atala; Peter M. Lovett; Clayton T. Morrison; Mihai Surdeanu; | arxiv-cs.CL | 2021-12-16 |
541 | Knowledge-Augmented Language Models for Cause-Effect Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate the augmentation of pretrained language models with commonsense knowledge in the cause-effect relation classification and commonsense causal reasoning tasks. |
Pedram Hosseini; David A. Broniatowski; Mona Diab; | arxiv-cs.CL | 2021-12-15 |
542 | An Empirical Study on Relation Extraction in The Biomedical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing models are defined for relation extraction in the general domain. |
Yongkang Li; | arxiv-cs.CL | 2021-12-10 |
543 | Automated Tabulation of Clinical Trial Results: A Joint Entity and Relation Extraction Approach with Transformer-based Language Representations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper investigates automating evidence table generation by decomposing the problem across two language processing tasks: \textit{named entity recognition}, which identifies key entities within text, such as drug names, and \textit{relation extraction}, which maps their relationships for separating them into ordered tuples. |
Jetsun Whitton; Anthony Hunter; | arxiv-cs.CL | 2021-12-10 |
544 | Document-Level Biomedical Relation Extraction with Generative Adversarial Network and Dual-Attention Multi-Instance Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction (RE) aims to extract relations among entities within a document, which is more complex than its sentence-level counterpart, especially in … |
Lishuang Li; Ruiyuan Lian; Hongbin Lu; | 2021 IEEE International Conference on Bioinformatics and … | 2021-12-09 |
545 | Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, We propose a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize unseen relations under the zero-shot setting. |
Jiaying Gong; Hoda Eldardiry; | arxiv-cs.CL | 2021-12-08 |
546 | From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to learn not only from the consensus but also the disagreement among different models in SSRE. |
Wanli Li; Tieyun Qian; | arxiv-cs.CL | 2021-12-02 |
547 | Context-Dependent Semantic Parsing for Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose SMARTER, a neural semantic parser, to extract temporal information in text effectively. |
Bo-Ying Su; Shang-Ling Hsu; Kuan-Yin Lai; Jane Yung-jen Hsu; | arxiv-cs.CL | 2021-12-01 |
548 | Text Mining Drug/Chemical-Protein Interactions Using An Ensemble of BERT and T5 Based Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For this relation extraction task, we attempt both a BERT-based sentence classification approach, and a more novel text-to-text approach using a T5 model. |
Virginia Adams; Hoo-Chang Shin; Carol Anderson; Bo Liu; Anas Abidin; | arxiv-cs.CL | 2021-11-30 |
549 | Predicting Document Coverage for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? |
Sneha Singhania; Simon Razniewski; Gerhard Weikum; | arxiv-cs.CL | 2021-11-26 |
550 | Multi-Attribute Relation Extraction (MARE) — Simplifying The Application of Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. |
Lars Klöser; Philipp Kohl; Bodo Kraft; Albert Zündorf; | arxiv-cs.CL | 2021-11-17 |
551 | Learning Logic Rules for Document-level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules. |
DONGYU RU et. al. | arxiv-cs.CL | 2021-11-09 |
552 | Zero-Shot Information Extraction As A Unified Text-to-Triple Translation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We cast a suite of information extraction tasks into a text-to-triple translation framework. |
CHENGUANG WANG et. al. | emnlp | 2021-11-05 |
553 | Incorporating Medical Knowledge in BERT for Clinical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve these issues, in this research, we conduct a comprehensive examination of different techniques to add medical knowledge into a pre-trained BERT model for clinical relation extraction. |
Arpita Roy; Shimei Pan; | emnlp | 2021-11-05 |
554 | Modular Self-Supervision for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose decomposing document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. |
SHENG ZHANG et. al. | emnlp | 2021-11-05 |
555 | Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. |
JIANZHU BAO et. al. | emnlp | 2021-11-05 |
556 | Large-Scale Relation Learning for Question Answering Over Knowledge Bases with Pre-trained Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge the gap between the natural language and the structured KB, we propose three relation learning tasks for BERT-based KBQA, including relation extraction, relation matching, and relation reasoning. |
YUANMENG YAN et. al. | emnlp | 2021-11-05 |
557 | Relation Extraction with Word Graphs from N-grams IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods with an unsupervised manner to build the context graph, without relying on the existence of a dependency parser. |
Han Qin; Yuanhe Tian; Yan Song; | emnlp | 2021-11-05 |
558 | Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To alleviate selection bias due to the lack of feedback loops in existing LRE learning paradigms, we developed a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate the gradient descent direction on labeled data and bootstrap its optimization capability through trial and error. |
XUMING HU et. al. | emnlp | 2021-11-05 |
559 | Separating Retention from Extraction in The Evaluation of End-to-end Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose two experiments confirming that retention of known facts is a key factor of performance on standard benchmarks. |
Bruno Taill?; Vincent Guigue; Geoffrey Scoutheeten; Patrick Gallinari; | emnlp | 2021-11-05 |
560 | Distantly Supervised Relation Extraction Using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Multi-Layer Revision Network (MLRN) which alleviates the effects of word-level noise by emphasizing inner-sentence correlations before extracting relevant information within sentences. |
Xiangyu Lin; Tianyi Liu; Weijia Jia; Zhiguo Gong; | emnlp | 2021-11-05 |
561 | Distilling Relation Embeddings from Pretrained Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. |
Asahi Ushio; Jose Camacho-Collados; Steven Schockaert; | emnlp | 2021-11-05 |
562 | CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in The Wild IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present the problem of cross-document RE, making an initial step towards knowledge acquisition in the wild. |
YUAN YAO et. al. | emnlp | 2021-11-05 |
563 | On The Relation Between Syntactic Divergence and Zero-Shot Performance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. |
Ofir Arviv; Dmitry Nikolaev; Taelin Karidi; Omri Abend; | emnlp | 2021-11-05 |
564 | Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a joint model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. |
Haoyang Wen; Heng Ji; | emnlp | 2021-11-05 |
565 | Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. |
Oscar Sainz; Oier Lopez de Lacalle; Gorka Labaka; Ander Barrena; Eneko Agirre; | emnlp | 2021-11-05 |
566 | Exploring Task Difficulty for Few-Shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information. |
Jiale Han; Bo Cheng; Wei Lu; | emnlp | 2021-11-05 |
567 | A Relation-Oriented Clustering Method for Open Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a relation-oriented clustering model and use it to identify the novel relations in the unlabeled data. |
Jun Zhao; Tao Gui; Qi Zhang; Yaqian Zhou; | emnlp | 2021-11-05 |
568 | Entity Relation Extraction As Dependency Parsing in Visually Rich Documents IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. |
YUE ZHANG et. al. | emnlp | 2021-11-05 |
569 | A Partition Filter Network for Joint Entity and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter. |
Zhiheng Yan; Chong Zhang; Jinlan Fu; Qi Zhang; Zhongyu Wei; | emnlp | 2021-11-05 |
570 | MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. |
Manqing Dong; Chunguang Pan; Zhipeng Luo; | emnlp | 2021-11-05 |
571 | Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: First, it finds out possible FN samples by heuristically leveraging the memory mechanism of deep neural networks. Then, it aligns those unlabeled data with the training data into a unified feature space by adversarial training to assign pseudo labels and further utilize the information contained in them. |
Kailong Hao; Botao Yu; Wei Hu; | emnlp | 2021-11-05 |
572 | Graph Based Network with Contextualized Representations of Turns in Dialogue IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. |
Bongseok Lee; Yong Suk Choi; | emnlp | 2021-11-05 |
573 | Unsupervised Relation Extraction: A Variational Autoencoder Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable. |
Chenhan Yuan; Hoda Eldardiry; | emnlp | 2021-11-05 |
574 | Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA). |
XINCHENG JU et. al. | emnlp | 2021-11-05 |
575 | Towards Realistic Few-Shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations. |
Sam Brody; Sichao Wu; Adrian Benton; | emnlp | 2021-11-05 |
576 | Extracting Event Temporal Relations Via Hyperbolic Geometry IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. |
Xingwei Tan; Gabriele Pergola; Yulan He; | emnlp | 2021-11-05 |
577 | Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a novel synchronous dual network (SDN) with cross-type attention via separately and interactively considering the entity types and relation types. |
Hui Wu; Xiaodong Shi; | emnlp | 2021-11-05 |
578 | SERC: Syntactic and Semantic Sequence Based Event Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: So we propose a joint model that incorporates both temporal and causal features to perform causal relation classification. |
Kritika Venkatachalam; Raghava Mutharaju; Sumit Bhatia; | arxiv-cs.CL | 2021-11-03 |
579 | Diagnosing Errors in Video Relation Detectors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a diagnostic tool for analyzing the sources of detection errors. |
Shuo Chen; Pascal Mettes; Cees G. M. Snoek; | arxiv-cs.CV | 2021-10-25 |
580 | Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types. |
WILLIAM HOGAN et. al. | arxiv-cs.CL | 2021-10-24 |
581 | A Review and Outlook for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) aims to identify and determine the specific relation between entity pairs from natural language texts. As a key technology of Natural Language Processing … |
Yulan Yan; Haolin Sun; Jie Liu; | Proceedings of the 5th International Conference on Computer … | 2021-10-19 |
582 | Entity Relation Extraction As Dependency Parsing in Visually Rich Documents IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. |
YUE ZHANG et. al. | arxiv-cs.CL | 2021-10-19 |
583 | A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we present IndoRE, a dataset with 21K entity and relation tagged gold sentences in three Indian languages, plus English. We release the dataset for future research. |
Arijit Nag; Bidisha Samanta; Animesh Mukherjee; Niloy Ganguly; Soumen Chakrabarti; | arxiv-cs.CL | 2021-10-18 |
584 | A Data Bootstrapping Recipe for Low-Resource Multilingual Relation Classification IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification (sometimes called ‘extraction’) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is … |
Arijit Nag; Bidisha Samanta; Animesh Mukherjee; Niloy Ganguly; Soumen Chakrabarti; | CONLL | 2021-10-18 |
585 | PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In response, we explore a simple baseline approach (PARE) in which all sentences of a bag are concatenated into a passage of sentences, and encoded jointly using BERT. |
Vipul Rathore; Kartikeya Badola; Parag Singla; | arxiv-cs.CL | 2021-10-14 |
586 | Social Fabric: Tubelet Compositions for Video Relation Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Where existing works treat object proposals or tubelets as single entities and model their relations a posteriori, we propose to classify and detect predicates for pairs of object tubelets a priori. |
Shuo Chen; Zenglin Shi; Pascal Mettes; Cees G. M. Snoek; | iccv | 2021-10-08 |
587 | FoodChem: A Food-chemical Relation Extraction Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present FoodChem, a new Relation Extraction (RE) model for identifying chemicals present in the composition of food entities, based on textual information provided in biomedical peer-reviewed scientific literature. |
Gjorgjina Cenikj; Barbara Koroušić Seljak; Tome Eftimov; | arxiv-cs.CL | 2021-10-05 |
588 | MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we present a materials-aware language model, namely, MatSciBERT, which is trained on a large corpus of scientific literature published in the materials domain. |
Tanishq Gupta; Mohd Zaki; N. M. Anoop Krishnan; | arxiv-cs.CL | 2021-09-30 |
589 | Separating Retention from Extraction in The Evaluation of End-to-end Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose several experiments confirming that retention of known facts is a key factor of performance on standard benchmarks. |
Bruno Taillé; Vincent Guigue; Geoffrey Scoutheeten; Patrick Gallinari; | arxiv-cs.CL | 2021-09-24 |
590 | DisCoDisCo at The DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. |
LUKE GESSLER et. al. | arxiv-cs.CL | 2021-09-20 |
591 | Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel model to enrich distantly-supervised sentences with entity types. |
Yang Li; Guodong Long; Tao Shen; Jing Jiang; | arxiv-cs.CL | 2021-09-18 |
592 | Slot Filling for Biomedical Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we present a slot filling approach to the task of biomedical IE, effectively replacing the need for entity and relation-specific training data, allowing us to deal with zero-shot settings. |
Yannis Papanikolaou; Marlene Staib; Justin Grace; Francine Bennett; | arxiv-cs.CL | 2021-09-17 |
593 | Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce SOLS, a novel model which can explicitly induce speaker-oriented latent structures for better DiaRE. |
Guoshun Nan; Guoqing Luo; Sicong Leng; Yao Xiao; Wei Lu; | arxiv-cs.CL | 2021-09-11 |
594 | D-REX: Dialogue Relation Extraction with Explanations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that explains and ranks relations. |
Alon Albalak; Varun Embar; Yi-Lin Tuan; Lise Getoor; William Yang Wang; | arxiv-cs.CL | 2021-09-10 |
595 | Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. |
Oscar Sainz; Oier Lopez de Lacalle; Gorka Labaka; Ander Barrena; Eneko Agirre; | arxiv-cs.CL | 2021-09-08 |
596 | Semi-Automated Labeling of Requirement Datasets for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a flexible, semi-automatic framework for labeling data for relation extraction. Furthermore, we provide a dataset of preprocessed sentences from the requirements engineering domain, including a set of automatically created as well as hand-crafted labels. |
Jeremias Bohn; Jannik Fischbach; Martin Schmitt; Hinrich Schütze; Andreas Vogelsang; | arxiv-cs.SE | 2021-09-05 |
597 | Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. |
Christos Theodoropoulos; James Henderson; Andrei C. Coman; Marie-Francine Moens; | arxiv-cs.CL | 2021-09-02 |
598 | Extracting All Aspect-polarity Pairs Jointly in A Text with Relation Extraction Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the perspective, we present a position- and aspect-aware sequence2sequence model for joint extraction of aspect-polarity pairs. |
Lingmei Bu; Li Chen; Yongmei Lu; Zhonghua Yu; | arxiv-cs.CL | 2021-09-01 |
599 | TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, trigger annotations may not be always available for the target data, so it is challenging to leverage such information for enhancing the performance. Therefore, this paper proposes to learn how to identify triggers from the data with trigger annotations and then transfers the trigger-finding capability to other datasets for better performance. |
Po-Wei Lin; Shang-Yu Su; Yun-Nung Chen; | arxiv-cs.CL | 2021-08-31 |
600 | Relation Extraction from Tables Using Artificially Generated Metadata Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this issue, we propose methods to artificially create some of this metadata for synthetic tables. |
Gaurav Singh; Siffi Singh; Joshua Wong; Amir Saffari; | arxiv-cs.CL | 2021-08-24 |
601 | Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a self-ensemble filtering mechanism to filter out the noisy samples during the training process. |
Tapas Nayak; Navonil Majumder; Soujanya Poria; | arxiv-cs.CL | 2021-08-22 |
602 | A Hierarchical Entity Graph Convolutional Network for Relation Extraction Across Documents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose cross-document relation extraction, where the two entities of a relation tuple appear in two different documents that are connected via a chain of common entities. Following this idea, we create a dataset for two-hop relation extraction, where each chain contains exactly two documents. |
Tapas Nayak; Hwee Tou Ng; | arxiv-cs.CL | 2021-08-21 |
603 | Extracting Radiological Findings With Normalized Anatomical Information Using A Span-Based BERT Relation Extraction Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explore the extraction and normalization of anatomical information in radiology reports that is associated with radiological findings. |
Kevin Lybarger; Aashka Damani; Martin Gunn; Ozlem Uzuner; Meliha Yetisgen; | arxiv-cs.CL | 2021-08-20 |
604 | SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Given the lack of reliable ground truth data, we present SoMeSci (Software Mentions in Science) a gold standard knowledge graph of software mentions in scientific articles. |
David Schindler; Felix Bensmann; Stefan Dietze; Frank Krüger; | arxiv-cs.IR | 2021-08-20 |
605 | Video Relation Detection Via Tracklet Based Visual Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we apply the state-of-the-art video object tracklet detection pipeline MEGA and deepSORT to generate tracklet proposals. |
Kaifeng Gao; Long Chen; Yifeng Huang; Jun Xiao; | arxiv-cs.CV | 2021-08-19 |
606 | RTE: A Tool for Annotating Relation Triplets from Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a Web-based annotation tool `Relation Triplets Extractor’ \footnote{https://abera87.github.io/annotate/} (RTE) for annotating relation triplets from the text. |
Ankan Mullick; Animesh Bera; Tapas Nayak; | arxiv-cs.CL | 2021-08-18 |
607 | An Effective System for Multi-format Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here we describe our system for this multi-format information extraction competition task. |
Yaduo Liu; Longhui Zhang; Shujuan Yin; Xiaofeng Zhao; Feiliang Ren; | arxiv-cs.CL | 2021-08-16 |
608 | MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in The Mobility Domain Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present MobIE, a German-language dataset, which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. |
Leonhard Hennig; Phuc Tran Truong; Aleksandra Gabryszak; | arxiv-cs.CL | 2021-08-16 |
609 | Consistent Inference for Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a consistent learning and inference method to minimize possible contradictions from those distinctions. |
Xinwei Long; Shuzi Niu; Yucheng Li; | ijcai | 2021-08-13 |
610 | Cross-Network Learning with Partially Aligned Graph Convolutional Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, I propose partially aligned graph convolutional networks to learn node representations across the models. |
Meng Jiang; | kdd | 2021-08-12 |
611 | Knowledge-Enhanced Domain Adaptation in Few-Shot Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Knowledge-Enhanced Few-shot RC model for the Domain Adaptation task (KEFDA), which incorporates general and domain-specific knowledge graphs (KGs) to the RC model to improve its domain adaptability. |
JIAWEN ZHANG et. al. | kdd | 2021-08-12 |
612 | Multi-Scale Feature and Metric Learning for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the above limitations, we propose a multi-scale feature and metric learning framework for relation extraction. |
Mi Zhang; Tieyun Qian; | arxiv-cs.CL | 2021-07-28 |
613 | UniRE: A Unified Label Space for Entity Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose to eliminate the different treatment on the two sub-tasks’ label spaces. |
YIJUN WANG et. al. | acl | 2021-07-26 |
614 | ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models Via Contrastive Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text. |
YUJIA QIN et. al. | acl | 2021-07-26 |
615 | CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we go beyond typical MIL framework and propose a novel contrastive instance learning (CIL) framework. |
TAO CHEN et. al. | acl | 2021-07-26 |
616 | Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To fully utilize memorized samples, in this paper, we employ relation prototype to extract useful information of each relation. |
LI CUI et. al. | acl | 2021-07-26 |
617 | How Knowledge Graph and Attention Help? A Qualitative Analysis Into Bag-level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). |
Zikun Hu; Yixin Cao; Lifu Huang; Tat-Seng Chua; | acl | 2021-07-26 |
618 | Entity Enhancement for Implicit Discourse Relation Classification in The Biomedical Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. |
Wei Shi; Vera Demberg; | acl | 2021-07-26 |
619 | Revisiting The Negative Data of Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we first provide a thorough analysis of the above challenges caused by negative data. Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem. |
CHENHAO XIE et. al. | acl | 2021-07-26 |
620 | From Discourse to Narrative: Knowledge Projection for Event Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. |
JIALONG TANG et. al. | acl | 2021-07-26 |
621 | How Knowledge Graph and Attention Help? A Quantitative Analysis Into Bag-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). |
Zikun Hu; Yixin Cao; Lifu Huang; Tat-Seng Chua; | arxiv-cs.CL | 2021-07-26 |
622 | Element Intervention for Open Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we revisit the procedure of OpenRE from a causal view. |
Fangchao Liu; Lingyong Yan; Hongyu Lin; Xianpei Han; Le Sun; | acl | 2021-07-26 |
623 | Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN). |
Yuanhe Tian; Guimin Chen; Yan Song; Xiang Wan; | acl | 2021-07-26 |
624 | SENT: Sentence-level Distant Relation Extraction Via Negative Training IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction. |
RUOTIAN MA et. al. | acl | 2021-07-26 |
625 | A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. |
Fei Li; ZhiChao Lin; Meishan Zhang; Donghong Ji; | acl | 2021-07-26 |
626 | Entity Concept-enhanced Few-shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this problem, in this paper, we proposed a novel entity CONCEPT-enhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance. |
Shan Yang; Yongfei Zhang; Guanglin Niu; Qinghua Zhao; Shiliang Pu; | acl | 2021-07-26 |
627 | Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). |
Tuan Lai; Heng Ji; ChengXiang Zhai; Quan Hung Tran; | acl | 2021-07-26 |
628 | Verb Metaphor Detection Via Contextual Relation Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that it is appropriate to view this task as relation classification between a verb and its various contexts. |
Wei Song; Shuhui Zhou; Ruiji Fu; Ting Liu; Lizhen Liu; | acl | 2021-07-26 |
629 | Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. |
QUZHE HUANG et. al. | acl | 2021-07-26 |
630 | TIMERS: Document-level Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present TIMERS – a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. |
PUNEET MATHUR et. al. | acl | 2021-07-26 |
631 | Improving Sentence-Level Relation Extraction Through Curriculum Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a curriculum learning-based relation extraction model that splits data by difficulty and utilizes them for learning. |
Seongsik Park; Harksoo Kim; | arxiv-cs.CL | 2021-07-20 |
632 | Clinical Relation Extraction Using Transformer-based Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The goal of this study is to systematically explore three widely used transformer-based models (i.e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain. |
Xi Yang; Zehao Yu; Yi Guo; Jiang Bian; Yonghui Wu; | arxiv-cs.CL | 2021-07-19 |
633 | What and When to Look?: Temporal Span Proposal Network for Video Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To date, two representative methods have been proposed to tackle Video Visual Relation Detection (VidVRD): segment-based and window-based. We first point out limitations of these methods and propose a novel approach named Temporal Span Proposal Network (TSPN). |
Sangmin Woo; Junhyug Noh; Kangil Kim; | arxiv-cs.CV | 2021-07-15 |
634 | ReadsRE: Retrieval-Augmented Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new paradigm named retrieval-augmented distantly supervised relation extraction (ReadsRE), which can incorporate large-scale open-domain knowledge (e.g., Wikipedia) into the retrieval step. |
Yue Zhang; Hongliang Fei; Ping Li; | sigir | 2021-07-13 |
635 | Position Enhanced Mention Graph Attention Network for Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle both local and speaker dependency challenges, we explicitly construct a unified mention co-occurrence graph within a local utterance window or all utterances of a speaker from different entities. |
Xinwei Long; Shuzi Niu; Yucheng Li; | sigir | 2021-07-13 |
636 | Injecting Knowledge Base Information Into End-to-End Joint Entity and Relation Extraction and Coreference Resolution IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We consider a joint information extraction (IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. |
Severine Verlinden; Klim Zaporojets; Johannes Deleu; Thomas Demeester; Chris Develder; | arxiv-cs.CL | 2021-07-05 |
637 | HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a Hybrid Span Generator (HySPA) that invertibly maps the information graph to an alternating sequence of nodes and edge types, and directly generates such sequences via a hybrid span decoder which can decode both the spans and the types recurrently in linear time and space complexities. |
Liliang Ren; Chenkai Sun; Heng Ji; Julia Hockenmaier; | arxiv-cs.CL | 2021-06-30 |
638 | A Simple and Efficient Probabilistic Language Model for Code-Mixed Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a simple probabilistic approach for building efficient word embedding for code-mixed text and exemplifying it over language identification of Hindi-English short test messages scrapped from Twitter. |
M Zeeshan Ansari; Tanvir Ahmad; M M Sufyan Beg; Asma Ikram; | arxiv-cs.CL | 2021-06-29 |
639 | Effective Cascade Dual-Decoder Model for Joint Entity and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an effective cascade dual-decoder approach to extract overlapping relational triples, which includes a text-specific relation decoder and a relation-corresponded entity decoder. |
Lianbo Ma; Huimin Ren; Xiliang Zhang; | arxiv-cs.CL | 2021-06-27 |
640 | RECON: Relation Extraction Using Knowledge Graph Context in A Graph Neural Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). |
ANSON BASTOS et. al. | www | 2021-06-25 |
641 | A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present a Trigger-Sense Memory Flow Framework (TriMF) for joint entity and relation extraction. |
Yongliang Shen; Xinyin Ma; Yechun Tang; Weiming Lu; | www | 2021-06-25 |
642 | SENT: Sentence-level Distant Relation Extraction Via Negative Training IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction. |
RUOTIAN MA et. al. | arxiv-cs.CL | 2021-06-22 |
643 | Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Objective: The objective of this study is to develop a deep learning pipeline to detect signals on dietary supplement-related adverse events (DS AEs) from Twitter. |
Yefeng Wang; Yunpeng Zhao; Jiang Bian; Rui Zhang; | arxiv-cs.CL | 2021-06-21 |
644 | Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. |
TAO CHEN et. al. | arxiv-cs.CL | 2021-06-20 |
645 | Graph-based Joint Pandemic Concern and Relation Extraction on Twitter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel end-to-end deep learning model to identify people’s concerns and the corresponding relations based on Graph Convolutional Network and Bi-directional Long Short Term Memory integrated with Concern Graph. |
Jingli Shi; Weihua Li; Sira Yongchareon; Yi Yang; Quan Bai; | arxiv-cs.CL | 2021-06-18 |
646 | Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an evidence-enhanced framework, Eider, that empowers DocRE by efficiently extracting evidence and effectively fusing the extracted evidence in inference. |
Yiqing Xie; Jiaming Shen; Sha Li; Yuning Mao; Jiawei Han; | arxiv-cs.CL | 2021-06-16 |
647 | UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document. |
HUANQIN WU et. al. | arxiv-cs.CL | 2021-06-09 |
648 | Document-level Relation Extraction As Semantic Segmentation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Herein, we propose a Document U-shaped Network for document-level relation extraction. |
NINGYU ZHANG et. al. | arxiv-cs.CL | 2021-06-07 |
649 | Let’s Be Explicit About That: Distant Supervision for Implicit Discourse Relation Classification Via Connective Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the current study, we perform implicit discourse relation classification without relying on any labeled implicit relation. |
Murathan Kurfalı; Robert Östling; | arxiv-cs.CL | 2021-06-06 |
650 | Let’s Be Explicit About That: Distant Supervision for Implicit Discourse Relation Classification Via Connective Prediction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In implicit discourse relation classification, we want to predict the relation between adjacent sentences in the absence of any overt discourse connectives. This is challenging … |
Murathan Kurfali; R. Ostling; | ArXiv | 2021-06-06 |
651 | SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: It is usually formulated as a classification problem that predicts relations for all entity pairs in the document. |
Shuang Zeng; Yuting Wu; Baobao Chang; | arxiv-cs.CL | 2021-06-03 |
652 | End-to-End Hierarchical Relation Extraction for Generic Form Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present a novel deep neural network to jointly perform both entity detection and link prediction in an end-to-end fashion. |
Tuan-Anh Nguyen Dang; Duc-Thanh Hoang; Quang-Bach Tran; Chih-Wei Pan; Thanh-Dat Nguyen; | arxiv-cs.AI | 2021-06-02 |
653 | Discriminative Reasoning for Document-level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel discriminative reasoning framework to explicitly model the paths of these reasoning skills between each entity pair in this document. |
Wang Xu; Kehai Chen; Tiejun Zhao; | arxiv-cs.CL | 2021-06-02 |
654 | KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). |
ABHISHEK NADGERI et. al. | arxiv-cs.CL | 2021-06-01 |
655 | PTR: Prompt Tuning with Rules for Text Classification IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose prompt tuning with rules (PTR) for many-class text classification and apply logic rules to construct prompts with several sub-prompts. |
Xu Han; Weilin Zhao; Ning Ding; Zhiyuan Liu; Maosong Sun; | arxiv-cs.CL | 2021-05-24 |
656 | Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a constraint graph to model the dependencies between relation labels. |
TIANMING LIANG et. al. | arxiv-cs.CL | 2021-05-24 |
657 | A Frustratingly Easy Approach for Entity and Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders. |
Zexuan Zhong; Danqi Chen; | naacl | 2021-05-23 |
658 | Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. |
Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou; | naacl | 2021-05-23 |
659 | Integrating Lexical Information Into Entity Neighbourhood Representations for Relation Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present an extension of OpenKi that incorporates embeddings of text-based representations of the entities and the relations. |
Ian Wood; Mark Johnson; Stephen Wan; | naacl | 2021-05-23 |
660 | ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we formulate the zero-shot relation extraction problem by incorporating the text description of seen and unseen relations. |
Chih-Yao Chen; Cheng-Te Li; | naacl | 2021-05-23 |
661 | Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE). |
Minh Van Nguyen; Viet Lai; Thien Huu Nguyen; | naacl | 2021-05-23 |
662 | Open Hierarchical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To establish the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task. |
KAI ZHANG et. al. | naacl | 2021-05-23 |
663 | Global Context for Improving Recognition of Online Handwritten Mathematical Expressions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a temporal classification method for all three subtasks of symbol segmentation, symbol recognition and relation classification in online handwritten mathematical expressions (HMEs). |
Cuong Tuan Nguyen; Thanh-Nghia Truong; Hung Tuan Nguyen; Masaki Nakagawa; | arxiv-cs.CV | 2021-05-21 |
664 | Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To evaluate DS-RE models in a more credible way, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones. |
TIANYU GAO et. al. | arxiv-cs.CL | 2021-05-20 |
665 | Boosting Span-based Joint Entity and Relation Extraction Via Squence Tagging Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By stacking multiple atten-tion layers in depth, we design a deep neu-ral architecture to build STSN, and each atten-tion layer consists of three basic attention units. |
Bin Ji; Shasha Li; Jie Yu; Jun Ma; Huijun Liu; | arxiv-cs.CL | 2021-05-20 |
666 | Do Models Learn The Directionality of Relations? A New Evaluation: Relation Direction Recognition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To explore the question, a novel evaluation task, called Relation Direction Recognition (RDR), is proposed to explore whether models learn the directionality of relations. |
Shengfei Lyu; Xingyu Wu; Jinlong Li; Qiuju Chen; Huanhuan Chen; | arxiv-cs.CL | 2021-05-19 |
667 | Relation Classification with Entity Type Restriction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel paradigm, RElation Classification with ENtity Type restriction (RECENT), which exploits entity types to restrict candidate relations. |
Shengfei Lyu; Huanhuan Chen; | arxiv-cs.CL | 2021-05-18 |
668 | Distantly Supervised Relation Extraction Via Recursive Hierarchy-Interactive Attention and Entity-Order Perception Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, we introduce a newfangled training objective, called Entity-Order Perception (EOP), to make the sentence encoder retain more entity appearance information. |
Ridong Han; Tao Peng; Jiayu Han; Hai Cui; Lu Liu; | arxiv-cs.CL | 2021-05-17 |
669 | Multi-Granularity Heterogeneous Graph for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Multi-granularity Heterogeneous Graph (MHG) to tackle this challenge. |
H. TANG et. al. | icassp | 2021-05-16 |
670 | More: A Metric Learning Based Framework for Open-Domain Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction). |
Y. Wang; R. Lou; K. Zhang; M. Y. Chen; Y. Yang; | icassp | 2021-05-16 |
671 | Multi-Entity Collaborative Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead of designing specific models for single relationship extraction tasks, this paper aims to propose a general framework to extract multiple relations among multiple entities in unstructured text by taking advantage of existing models. |
H. Liu; Z. Li; D. Sheng; H. -T. Zheng; Y. Shen; | icassp | 2021-05-16 |
672 | Ensemble Making Few-Shot Learning Stronger Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks. Many few-shot models have been widely used for relation learning tasks. … |
Qing Lin; Yongbin Liu; Wen Wen; Z. Tao; | Data Intelligence | 2021-05-12 |
673 | GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For this purpose, we acquire multimodal features from both textual data and layout information and build an end-to-end model through multitask training to combine word grouping and relation extraction to enhance performance on each task. |
ZILONG WANG et. al. | arxiv-cs.CL | 2021-05-10 |
674 | Discourse Relation Embeddings: Representing The Relations Between Discourse Segments in Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel method for automatically creating discourse relation embeddings (DiscRE), addressing the embedding challenge through a weakly supervised, multitask approach to learn diverse and nuanced relations between discourse segments in social media. |
Youngseo Son; Vasudha Varadarajan; H Andrew Schwartz; | arxiv-cs.CL | 2021-05-04 |
675 | Information Structures in Sociology Research Papers: Modeling Cause–effect and Comparison Relations in Research Objective and Result Statements 1 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into … |
Wei Cheng; Christopher S. G. Khoo; | Journal of the Association for Information Science and … | 2021-04-29 |
676 | Multi-view Inference for Relation Extraction with Uncertain Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes to exploit uncertain knowledge to improve relation extraction. |
Bo Li; Wei Ye; Canming Huang; Shikun Zhang; | arxiv-cs.CL | 2021-04-28 |
677 | Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction. |
Peng Su; Yifan Peng; K. Vijay-Shanker; | arxiv-cs.CL | 2021-04-28 |
678 | Explore BiLSTM-CRF-Based Models for Open Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop several Open RE models based on the bidirectional LSTM-CRF (BiLSTM-CRF) neural network and different contextualized word embedding methods. |
Tao Ni; Qing Wang; Gabriela Ferraro; | arxiv-cs.CL | 2021-04-25 |
679 | Enriched Attention for Robust Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address this problem with an enriched attention mechanism. |
Heike Adel; Jannik Strötgen; | arxiv-cs.CL | 2021-04-22 |
680 | Extracting Adverse Drug Events from Clinical Notes IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explore three approaches: a rule-based approach, a deep learning-based approach, and a contextualized language model-based approach. |
Darshini Mahendran; Bridget T. McInnes; | arxiv-cs.CL | 2021-04-21 |
681 | Improving Biomedical Pretrained Language Models with Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases. |
Zheng Yuan; Yijia Liu; Chuanqi Tan; Songfang Huang; Fei Huang; | arxiv-cs.CL | 2021-04-20 |
682 | Extracting Temporal Event Relation with Syntax-guided Graph Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: One of the key challenges of this problem is that when the events of interest are far away in text, the context in-between often becomes complicated, making it challenging to resolve the temporal relationship between them. This paper thus proposes a new Syntax-guided Graph Transformer network (SGT) to mitigate this issue, by (1) explicitly exploiting the connection between two events based on their dependency parsing trees, and (2) automatically locating temporal cues between two events via a novel syntax-guided attention mechanism. |
Shuaicheng Zhang; Lifu Huang; Qiang Ning; | arxiv-cs.CL | 2021-04-19 |
683 | DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In response, we propose a new dataset, DiS-ReX, which alleviates these issues. |
Abhyuday Bhartiya; Kartikeya Badola; | arxiv-cs.CL | 2021-04-17 |
684 | Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To remedy this, we propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC, and apply it to the TACRED dataset. |
Ofer Sabo; Yanai Elazar; Yoav Goldberg; Ido Dagan; | arxiv-cs.CL | 2021-04-17 |
685 | Re-TACRED: Addressing Shortcomings of The TACRED Dataset IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. |
George Stoica; Emmanouil Antonios Platanios; Barnabás Póczos; | arxiv-cs.CL | 2021-04-16 |
686 | KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt). |
XIANG CHEN et. al. | arxiv-cs.CL | 2021-04-15 |
687 | A Sample-Based Training Method for Distantly Supervised Relation Extraction with Pre-Trained Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel sampling method for DSRE that relaxes these hardware requirements. |
Mehrdad Nasser; Mohamad Bagher Sajadi; Behrouz Minaei-Bidgoli; | arxiv-cs.CL | 2021-04-15 |
688 | Representation Learning for Weakly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this thesis, we present several novel unsupervised pre-training models to learn the distributed text representation features, which are encoded with rich syntactic-semantic patterns of relation expressions. |
Zhuang Li; | arxiv-cs.CL | 2021-04-10 |
689 | UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements As Multi-Turn Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We approached these challenges by first identifying the quantities, extracting their units of measurement, classifying them with corresponding modifiers, and afterwards using them to jointly solve the last three subtasks in a multi-turn question answering manner. |
Andrei-Marius Avram; George-Eduard Zaharia; Dumitru-Clementin Cercel; Mihai Dascalu; | arxiv-cs.CL | 2021-04-09 |
690 | A Question-answering Based Framework for Relation Extraction Validation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore the possibility of using question answering as validation. |
Jiayang Cheng; Haiyun Jiang; Deqing Yang; Yanghua Xiao; | arxiv-cs.CL | 2021-04-07 |
691 | Deep Neural Networks for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation extraction from text is an important task for automatic knowledge base population. In this thesis, we first propose a syntax-focused multi-factor attention network model … |
Tapas Nayak; | arxiv-cs.CL | 2021-04-05 |
692 | Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents the system for SemEval 2021 Task 8 (MeasEval). |
Akash Gangwar; Sabhay Jain; Shubham Sourav; Ashutosh Modi; | arxiv-cs.CL | 2021-04-03 |
693 | Normal Vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we take the first step to leverage the salience-based method to analyze those adversarial samples. |
LUOQIU LI et. al. | arxiv-cs.CL | 2021-04-01 |
694 | A Global–Local Attentive Relation Detection Model for Knowledge-Based Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge-based question answering (KBQA) is an essential but challenging task for artificial intelligence and natural language processing. A key challenge pertains to the design … |
Chen Qiu; Guangyou Zhou; Z. Cai; Anders Søgaard; | IEEE Transactions on Artificial Intelligence | 2021-03-31 |
695 | Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To help future research, we present a comprehensive review of the recently published research works in relation extraction. |
Tapas Nayak; Navonil Majumder; Pawan Goyal; Soujanya Poria; | arxiv-cs.CL | 2021-03-31 |
696 | Multi-facet Universal Schema Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the violation of the USchema assumption, we propose multi-facet universal schema that uses a neural model to represent each sentence pattern as multiple facet embeddings and encourage one of these facet embeddings to be close to that of another sentence pattern if they co-occur with the same entity pair. |
Rohan Paul; Haw-Shiuan Chang; Andrew McCallum; | arxiv-cs.CL | 2021-03-29 |
697 | D-BERT: Incorporating Dependency-based Attention Into BERT for Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: National Key Research and Development Program of China, Grant/Award Number: 2016YFB1000905; the State Key Program of National Nature Science Foundation of China, Grant/Award … |
Yuan Huang; Zhixing Li; Weihui Deng; Guoyin Wang; Zhimin Lin; | CAAI Trans. Intell. Technol. | 2021-03-26 |
698 | Prototypical Representation Learning for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead of solely relying on the supervision from noisy labels, we propose to learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. |
NING DING et. al. | arxiv-cs.CL | 2021-03-22 |
699 | Structural Block Driven – Enhanced Convolutional Neural Representation for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel lightweight relation extraction approach of structural block driven – convolutional neural learning. |
Dongsheng Wang; Prayag Tiwari; Sahil Garg; Hongyin Zhu; Peter Bruza; | arxiv-cs.CL | 2021-03-21 |
700 | Leveraging Unlabeled Data for Entity-Relation Extraction Through Probabilistic Constraint Satisfaction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. |
Kareem Ahmed; Eric Wang; Guy Van den Broeck; Kai-Wei Chang; | arxiv-cs.LG | 2021-03-19 |
701 | Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE). |
Minh Van Nguyen; Viet Dac Lai; Thien Huu Nguyen; | arxiv-cs.CL | 2021-03-16 |
702 | Mention-centered Graph Neural Network for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. |
Jiaxin Pan; Min Peng; Yiyan Zhang; | arxiv-cs.CL | 2021-03-15 |
703 | Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we transform ASTE task into a multi-turn machine reading comprehension (MTMRC) task and propose a bidirectional MRC (BMRC) framework to address this challenge. |
Shaowei Chen; Yu Wang; Jie Liu; Yuelin Wang; | arxiv-cs.CL | 2021-03-13 |
704 | A Review on Semi-Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we review and compare three typical methods in semi-supervised RE with deep learning or meta-learning: self-ensembling, which forces consistent under perturbations but may confront insufficient supervision; self-training, which iteratively generates pseudo labels and retrain itself with the enlarged labeled set; dual learning, which leverages a primal task and a dual task to give mutual feedback. |
Yusen Lin; | arxiv-cs.CL | 2021-03-12 |
705 | Techniques for Jointly Extracting Entities and Relations: A Survey Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we survey various techniques for jointly extracting entities and relations. |
Sachin Pawar; Pushpak Bhattacharyya; Girish K. Palshikar; | arxiv-cs.CL | 2021-03-10 |
706 | Dual Pointer Network for Fast Extraction of Multiple Relations in A Sentence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this issue, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. |
Seongsik Park; Harksoo Kim; | arxiv-cs.CL | 2021-03-05 |
707 | Better Call The Plumber: Orchestrating Dynamic Information Extraction Pipelines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Plumber, the first framework that brings together the research community’s disjoint IE efforts. |
Mohamad Yaser Jaradeh; Kuldeep Singh; Markus Stocker; Andreas Both; Sören Auer; | arxiv-cs.CL | 2021-02-22 |
708 | REMOD: Relation Extraction for Modeling Online Discourse Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here we develop a novel supervised learning method for relation extraction that combines graph embedding techniques with path traversal on semantic dependency graphs. |
Matthew Sumpter; Giovanni Luca Ciampaglia; | arxiv-cs.SI | 2021-02-22 |
709 | Contextual Argument Component Classification for Class Discussions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions. |
Luca Lugini; Diane Litman; | arxiv-cs.CL | 2021-02-20 |
710 | Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we formulate such structure as distinctive dependencies between mention pairs. |
Benfeng Xu; Quan Wang; Yajuan Lyu; Yong Zhu; Zhendong Mao; | arxiv-cs.CL | 2021-02-19 |
711 | WebRED: Effective Pretraining And Finetuning For Relation Extraction On The Web IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We therefore introduce: WebRED (Web Relation Extraction Dataset), a strongly-supervised human annotated dataset for extracting relationships from a variety of text found on the World Wide Web, consisting of ~110K examples. |
Robert Ormandi; Mohammad Saleh; Erin Winter; Vinay Rao; | arxiv-cs.CL | 2021-02-18 |
712 | Two Training Strategies for Improving Relation Extraction Over Universal Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The code and datasets used in this paper are available at https://github.com/baodaiqin/UGDSRE. |
Qin Dai; Naoya Inoue; Ryo Takahashi; Kentaro Inui; | arxiv-cs.CL | 2021-02-12 |
713 | An End-to-end Model for Entity-level Relation Extraction Using Multi-instance Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a joint model for entity-level relation extraction from documents. |
Markus Eberts; Adrian Ulges; | arxiv-cs.CL | 2021-02-11 |
714 | Re-TACRED: Addressing Shortcomings of The TACRED Dataset IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. |
George Stoica; Emmanouil Antonios Platanios; Barnabas Poczos; | aaai | 2021-02-09 |
715 | KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the Knowledge-Enriched Meta-Learning (KEML) framework to address lexical relation classification. |
Chengyu Wang; Minghui Qiu; Jun Huang; Xiaofeng He; | aaai | 2021-02-09 |
716 | Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems. |
Wenxuan Zhou; Kevin Huang; Tengyu Ma; Jing Huang; | aaai | 2021-02-09 |
717 | DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the task of relation classification of interlocutors based on their dialogues. |
Qi Jia; Hongru Huang; Kenny Q. Zhu; | aaai | 2021-02-09 |
718 | Bootstrapping Relation Extractors Using Syntactic Search By Examples Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. |
Matan Eyal; Asaf Amrami; Hillel Taub-Tabib; Yoav Goldberg; | arxiv-cs.CL | 2021-02-09 |
719 | GDPNet: Refining Latent Multi-View Graph for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. |
Fuzhao Xue; Aixin Sun; Hao Zhang; Eng Siong Chng; | aaai | 2021-02-09 |
720 | Curriculum-Meta Learning for Order-Robust Continual Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction. |
TONGTONG WU et. al. | aaai | 2021-02-09 |
721 | Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. |
YICHAO ZHOU et. al. | aaai | 2021-02-09 |
722 | FL-MSRE: A Few-Shot Learning Based Approach to Multimodal Social Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the success of BERT, we propose a strong BERT based baseline to extract social relation from text only. |
HAI WAN et. al. | aaai | 2021-02-09 |
723 | Progressive Multi-task Learning with Controlled Information Flow for Joint Entity and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To achieve this objective, in this paper we design a multitask learning architecture based on the observation that correlations exist between outputs of some related tasks (e.g. entity recognition and relation extraction tasks), and they reflect the relevant features that need to be extracted from the input. |
Kai Sun; Richong Zhang; Samuel Mensah; Yongyi Mao; Xudong Liu; | aaai | 2021-02-09 |
724 | Visual Relation Detection Using Hybrid Analogical Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a new hybrid system for visual relation detection combining deep-learning models and analogical generalization. |
Kezhen Chen; Ken Forbus; | aaai | 2021-02-09 |
725 | Making The Relation Matters: Relation of Relation Learning Network for Sentence Semantic Matching IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. |
KUN ZHANG et. al. | aaai | 2021-02-09 |
726 | Document-Level Relation Extraction with Reconstruction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To alleviate this issue, we propose a novel encoder-classifier-reconstructor model for DocRE. |
Wang Xu; Kehai Chen; Tiejun Zhao; | aaai | 2021-02-09 |
727 | An Improved Baseline for Sentence-level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we revisit two problems that affect the performance of existing RE models, namely entity representation and noisy or ill-defined labels. |
Wenxuan Zhou; Muhao Chen; | arxiv-cs.CL | 2021-02-02 |
728 | Improving Distantly-Supervised Relation Extraction Through BERT-based Label & Instance Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose REDSandT (Relation Extraction with Distant Supervision and Transformers), a novel distantly-supervised transformer-based RE method, that manages to capture a wider set of relations through highly informative instance and label embeddings for RE, by exploiting BERT’s pre-trained model, and the relationship between labels and entities, respectively. |
Despina Christou; Grigorios Tsoumakas; | arxiv-cs.CL | 2021-02-01 |
729 | Is Depression Related to Cannabis?: A Knowledge-infused Model for Entity and Relation Extraction with Limited Supervision IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we explore the relationship between depression and consumption of cannabis in a targeted social media corpus involving personal use of cannabis with the intent to derive its potential mental health benefit. |
Kaushik Roy; Usha Lokala; Vedant Khandelwal; Amit Sheth; | arxiv-cs.CL | 2021-02-01 |
730 | LOME: Large Ontology Multilingual Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present LOME, a system for performing multilingual information extraction. |
PATRICK XIA et. al. | arxiv-cs.CL | 2021-01-28 |
731 | “Laughing at You or with You”: The Role of Sarcasm in Shaping The Disagreement Space Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive … |
Debanjan Ghosh; Ritvik Shrivastava; S. Muresan; | Conference of the European Chapter of the Association for … | 2021-01-26 |
732 | Laughing at You Or with You: The Role of Sarcasm in Shaping The Disagreement Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To further our understanding of the role of sarcasm in shaping the disagreement space, we present a thorough experimental setup using a corpus annotated with both argumentative moves (agree/disagree) and sarcasm. |
Debanjan Ghosh; Ritvik Shrivastava; Smaranda Muresan; | arxiv-cs.CL | 2021-01-26 |
733 | Process-Level Representation of Scientific Protocols with Interactive Annotation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments. |
Ronen Tamari; Fan Bai; Alan Ritter; Gabriel Stanovsky; | arxiv-cs.CL | 2021-01-25 |
734 | Video Relation Detection with Trajectory-aware Multi-modal Features IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present video relation detection with trajectory-aware multi-modal features to solve this task. |
Wentao Xie; Guanghui Ren; Si Liu; | arxiv-cs.CV | 2021-01-20 |
735 | HIVE-4-MAT: Advancing The Ontology Infrastructure for Materials Science Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Introduces HIVE-4-MAT – Helping Interdisciplinary Vocabulary Engineering for Materials Science, an automatic linked data ontology application. Covers contextual background for … |
Jane Greenberg; Xintong Zhao; Joseph Adair; Joan Boone; Xiaohua Tony Hu; | arxiv-cs.DL | 2021-01-19 |
736 | A Survey on Extraction of Causal Relations from Natural Language Text IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct a comprehensive survey of causality extraction. |
Jie Yang; Soyeon Caren Han; Josiah Poon; | arxiv-cs.IR | 2021-01-16 |
737 | Structured Prediction As Translation Between Augmented Natural Languages IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking. |
GIOVANNI PAOLINI et. al. | arxiv-cs.LG | 2021-01-14 |
738 | EventPlus: A Temporal Event Understanding Pipeline IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction. |
MINGYU DEREK MA et. al. | arxiv-cs.CL | 2021-01-13 |
739 | BERT-GT: Cross-sentence N-ary Relation Extraction with BERT and Graph Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel architecture that combines Bidirectional Encoder Representations from Transformers with Graph Transformer (BERT-GT), through integrating a neighbor-attention mechanism into the BERT architecture. |
Po-Ting Lai; Zhiyong Lu; | arxiv-cs.CL | 2021-01-11 |
740 | Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To make full use of the existing information and get a better feature representation for each instance, we propose to encode each class prototype in an adaptive way from two aspects. |
Yan Xiao; Yaochu Jin; Kuangrong Hao; | arxiv-cs.CL | 2021-01-10 |
741 | Learning Better Sentence Representation with Syntax Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach to combining syntax information with a pre-trained language model. |
Chen Yang; | arxiv-cs.CL | 2021-01-09 |
742 | Deep Neural Network Based Relation Extraction: An Overview Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design sentence encoder and de-noise method. |
Hailin Wang; Ke Qin; Rufai Yusuf Zakari; Guoming Lu; Jin Yin; | arxiv-cs.CL | 2021-01-06 |
743 | Curriculum-Meta Learning for Order-Robust Continual Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction. |
TONGTONG WU et. al. | arxiv-cs.CL | 2021-01-06 |
744 | NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision can generate large-scale relation classification data quickly and economically. However, a great number of noise sentences are introduced which can not express … |
Tielin Shen; Daling Wang; Shi Feng; Yifei Zhang; | 2021-01-01 | |
745 | A Unified Representation Learning Strategy for Open Relation Extraction with Ranked List Loss Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Open Relation Extraction (OpenRE), aiming to extract relational facts from open-domain corpora, is a sub-task of Relation Extraction and a crucial upstream process for many other … |
RENZE LOU et. al. | 2021-01-01 | |
746 | ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework. However, … |
YIJUN WANG et. al. | 2021-01-01 | |
747 | Context, Structure and Syntax-aware RST Discourse Parsing Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper describes the architecture of a shift-reduce parsing framework that (1) leverages the representational powers of BERT and Hierarchical Attention Networks (HANs) and (2) … |
Takshak Desai; Dan I. Moldovan; | 2021 IEEE 15th International Conference on Semantic … | 2021-01-01 |
748 | A Relation-aware Attention Neural Network for Modeling The Usage of Scientific Online Resources Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: More and more online resources for computer science are introduced, used and released in scientific literature in recent years. Knowledge about the usage of these online resources … |
YONGXIU XU et. al. | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
749 | Chinese Entity Relation Classification Via Fusing Granularity Information and Gated Recurrent Mechanism Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity relation classification is a foundational natural language processing task, which plays an important role in text analysis. At present, most of the methods for Chinese … |
Zicheng Zhong; | 2021 2nd International Conference on Artificial … | 2021-01-01 |
750 | Language Adaptation for Entity Relation Classification Via Adversarial Neural Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity relation classification aims to classify the semantic relationship between two marked entities in a given sentence, and plays a vital role in various natural language … |
Bo-Wei Zou; Rong-Tao Huang; Zeng-Zhuang Xu; Yu Hong; Guo-Dong Zhou; | Journal of Computer Science and Technology | 2021-01-01 |
751 | Chinese Relation Extraction on Forestry Knowledge Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qi Yue; Xiang Li; Dan Li; | Comput. Syst. Sci. Eng. | 2021-01-01 |
752 | Knowledge-Based Diverse Feature Transformation for Few-Shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
YUBAO TANG et. al. | 2021-01-01 | |
753 | Representation Iterative Fusion Based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text. However, few existing … |
Kang Zhao; Hua Xu; Yue Cheng; Xiaoteng Li; Kai Gao; | Knowl. Based Syst. | 2021-01-01 |
754 | Open Temporal Relation Extraction for Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA). … |
CHAO SHANG et. al. | Conference on Automated Knowledge Base Construction | 2021-01-01 |
755 | Cross-domain Limitations of Neural Models on Biomedical Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) aims to extract relational facts from plain text, which is essential to the biomedical research field with the rapid growth of biomedical literature and … |
I. Alimova; Elena Tutubalina; S. Nikolenko; | IEEE Access | 2021-01-01 |
756 | Gold Panning: Automatic Extraction of Scientific Information from Publications Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The millions of scientific papers published every year have made it well beyond any human’s ability to read and curate. As a result, valuable data and potentially ground-breaking … |
Zhi Hong; Kyle Chard; Ian T. Foster; | 2021 IEEE 17th International Conference on eScience … | 2021-01-01 |
757 | Chinese Triple Extraction Based on BERT Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Information extraction (IE) plays a crucial role in natural language processing, which extracts structured facts like entities, attributes, relations and events from unstructured … |
Weidong Deng; Yun Liu; | 2021 15th International Conference on Ubiquitous … | 2021-01-01 |
758 | Relation Extraction for Coal Mine Safety Information Using Recurrent Neural Networks with Bidirectional Minimal Gated Unit Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The data of coal mine safety field are massive, multi-source and heterogeneous. It is of practical importance to extract information from big data to achieve disaster precaution … |
Xiulei Liu; Shoulu Hou; Zhihui Qin; Sihan Liu; Jian Zhang; | EURASIP Journal on Wireless Communications and Networking | 2021-01-01 |
759 | GenerativeRE: Incorporating A Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Previous neural seq2seq models have shown the effectiveness for jointly extracting relation triplets. However, most of these models suffer from incompletion and disorder problems … |
Jiarun Cao; S. Ananiadou; | EMNLP | 2021-01-01 |
760 | Transformer with Local-feature Extractor for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Many pre-trained models recently achieved successful results in many NLP tasks. However, such models with heavy structure often led to problems such as a great amount of training … |
Lihan Liu; Pengfei Li; | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
761 | Macro Discourse Relation Recogniztion Based on Micro Discourse Structure and Self-Interactive Attention Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Macro discourse relation recognition is an important task of macro discourse analysis. The existing models ignore the micro discourse structure within paragraphs and could not … |
Yaxin Fan; Feng Jiang; Peifeng Li; Qiaoming Zhu; | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
762 | CovRelex: A COVID-19 Retrieval System with Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. This work aims at … |
VU TRAN et. al. | 2021-01-01 | |
763 | DiagramNet: Hand-Drawn Diagram Recognition Using Visual Arrow-Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View |
Bernhard Schäfer; Heiner Stuckenschmidt; | 2021-01-01 | |
764 | CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper presents our wining contribution to SemEval 2021 Task 8: MeasEval. The purpose of this task is identifying the counts and measurements from clinical scientific … |
JIARUN CAO et. al. | 2021-01-01 | |
765 | End-to-End Argument Mining As Biaffine Dependency Parsing Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is … |
Yuxiao Ye; Simone Teufel; | 2021-01-01 | |
766 | Gated Convolutional Neural Networks with Sentence-Related Selection for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yufeng Chen; Siqi Li; Xingya Li; Jinan Xu; Jian Liu; | IEICE Transactions on Information and Systems | 2021-01-01 |
767 | Leveraging Syntactic Dependency and Lexical Similarity for Neural Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yashen Wang; | 2021-01-01 | |
768 | Topological Relation Detection Technology of Substation Wiring Diagram in Electric Power System Related Papers Related Patents Related Grants Related Venues Related Experts View |
LI HAO et. al. | Journal of Beijing University of Aeronautics and … | 2021-01-01 |
769 | ESRE: Handling Repeated Entities in Distant Supervised Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervised relation extraction has been widely used to find novel relational facts from unstructured text. As far as we know, nearly all existing relation extraction … |
Xin Sun; Jinghu Jiang; Yuming Shang; | Neural Comput. Appl. | 2021-01-01 |
770 | Multi-Attribute Relation Extraction (MARE): Simplifying The Application of Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. … |
Lars Klöser; Philipp Kohl; Bodo Kraft; Albert Zündorf; | ArXiv | 2021-01-01 |
771 | HAIN: Hierarchical Aggregation and Inference Network for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Nan Hu; Taolin Zhang; Shuangji Yang; Wei Nong; Xiaofeng He; | 2021-01-01 | |
772 | Entity-Aware Relation Representation Learning for Open Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zihao Liu; Yan Zhang; Huizhen Wang; Jingbo Zhu; | 2021-01-01 | |
773 | Chinese Entity Relation Extraction Based on Multi-level Gated Recurrent Mechanism and Self-attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: There is the influence of polysemy and segmentation quality on semantic understanding for Chinese entity relation extraction, we propose a Chinese entity relation extraction model … |
Zicheng Zhong; | 2021 2nd International Conference on Artificial … | 2021-01-01 |
774 | Towards An Entity Relation Extraction Framework in The Cross-lingual Context Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Purpose Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource … |
Chuanming Yu; Haodong Xue; Manyi Wang; Lu An; | The Electronic Library | 2021-01-01 |
775 | Entity Relation Extraction Based on Multi-attention Mechanism and BiGRU Network Related Papers Related Patents Related Grants Related Venues Related Experts View |
Lingyun Wang; Caiquan Xiong; Wenxiang Xu; Song Lin; | 2021-01-01 | |
776 | An End-to-End Method for Joint Extraction of Tibetan Entity Relations Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity relation extraction is to find entities and relations from unstructured texts, which is beneficial to the applications of knowledge graphs and question answering systems. … |
Yuan Sun; Sisi Liu; Tianci Xia; Xiaobing Zhao; | Journal of Computer and Communications | 2021-01-01 |
777 | Joint Entity and Relation Extraction Model Based on Rich Semantics IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhiqiang Geng; Yanhui Zhang; Yongming Han; | Neurocomputing | 2021-01-01 |
778 | 3-D Relation Network for Visual Relation Recognition in Videos IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video visual relation recognition aims at mining the dynamic relation instances between objects in the form of 〈 subject , predicate , object 〉 , such as “person1-towards-person2” … |
Qianwen Cao; Heyan Huang; Xindi Shang; Boran Wang; Tat-Seng Chua; | Neurocomputing | 2021-01-01 |
779 | End-to-end Drug Entity Recognition and Adverse Effect Relation Extraction Via Principal Neighbourhood Aggregation Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Drug entity and adverse effect relation extraction is a critical task that aims to recognize drug entity and extract adverse effect relation from the unstructured medical text. … |
Luyue Kong; Qinghan Lai; Song Liu; | Journal of Physics: Conference Series | 2021-01-01 |
780 | Video Visual Relation Detection Via Iterative Inference IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The core problem of video visual relation detection (VidVRD) lies in accurately classifying the relation triplets, which comprise of the classes of subject and object entities, … |
Xindi Shang; Yicong Li; Junbin Xiao; Wei Ji; Tat-Seng Chua; | Proceedings of the 29th ACM International Conference on … | 2021-01-01 |
781 | WRTRe: Weighted Relative Position Transformer for Joint Entity and Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity and relation extraction is a critical task of information extraction in natural language processing. With fast developments of deep learning, this area has attracted great … |
Wei Zheng; Zhen Wang; Quanming Yao; Xuelong Li; | Neurocomputing | 2021-01-01 |
782 | Joint Extraction of Entities and Overlapping Relations Using Source-target Entity Labeling IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint extraction of entities and overlapping relations has attracted considerable attention in recent research. Existing relation extraction methods rely on a training set that is … |
Tingting Hang; Jun Feng; Yirui Wu; Le Yan; Yunfeng Wang; | Expert Syst. Appl. | 2021-01-01 |
783 | Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature Via Language Representation Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Understanding of nerve-organ interactions is crucial to facilitate the development of effective bioelectronic treatments. Towards the end of developing a systematized and … |
Ibrahim Burak Ozyurt; Joseph Menke; Anita Bandrowski; Maryann Martone; | Proceedings of the Second Workshop on Scholarly Document … | 2021-01-01 |
784 | Traffic Flow Estimation Based on Toll Ticket Data Considering Multitype Vehicle Impact Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: AbstractTraffic flow estimation (TFE) plays an important role in transportation systems, especially in relation to the detection, planning, and management of traffic for … |
Enjian Yao; Xiaowen Wang; Yang Yang; Long Pan; Yuanyuan Song; | 2021-01-01 | |
785 | From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: “Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most … |
FANGCHAO LIU et. al. | 2021-01-01 | |
786 | Automatic Classification and Entity Relation Detection in Hungarian Spinal MRI Reports Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A great number of radiologic reports are created each year which incorporate the expertise of radiologists. This knowledge could be exploited via machine understanding. This could … |
András Kicsi; Klaudia Szabó Ledenyi; Péter Pusztai; László Vidács; | 2021 IEEE/ACM 3rd International Workshop on Software … | 2021-01-01 |
787 | Traditional Chinese Medicine Entity Relation Extraction Based on CNN with Segment Attention IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting medical entity relations from Traditional Chinese Medicine (TCM) related article is crucial to connect domain knowledge between TCM with modern medicine. Herb accounts … |
Tian Bai; Haotian Guan; Shang Wang; Ye Wang; Lan Huang; | Neural Computing and Applications | 2021-01-01 |
788 | Graph-based Reasoning Model for Multiple Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Linguistic knowledge is useful for various NLP tasks, but the difficulty lies in the representation and application. We consider that linguistic knowledge is implied in a … |
Heyan Huang; Ming Lei; Chong Feng; | Neurocomputing | 2021-01-01 |
789 | Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The use of administrative documents to communicate and leave record of business information requires of methods able to automatically extract and understand the content from such … |
Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornés; Josep Lladós; | 2020 25th International Conference on Pattern Recognition … | 2021-01-01 |
790 | Neural Sequential Transfer Learning for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Christoph Alt; | 2021-01-01 | |
791 | KnowFi: Knowledge Extraction from Long Fictional Texts Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge base construction has recently been extended to fictional domains like multivolume novels and TV/movie series, aiming to support explorative queries for fans and … |
C. Chu; S. Razniewski; G. Weikum; | Conference on Automated Knowledge Base Construction | 2021-01-01 |
792 | Document-level Relation Extraction with Entity-Selection Attention IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction is a complex natural language processing task that predicts relations of entity pairs by capturing the critical semantic features on entity … |
Changsen Yuan; Heyan Huang; Chong Feng; Ge Shi; Xiaochi Wei; | Inf. Sci. | 2021-01-01 |
793 | Robust Neural Relation Extraction Via Multi-Granularity Noises Reduction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision is widely used to extract relational facts with automatically labeled datasets to reduce high cost of human annotation. However, current distantly supervised … |
Xinsong Zhang; Tianyi Liu; Pengshuai Li; Weijia Jia; Hai Zhao; | IEEE Transactions on Knowledge and Data Engineering | 2021-01-01 |
794 | DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Scientific documents are replete with measurements mentioned in various formats and styles. As such, in a document with multiple quantities and measured entities, the task of … |
Amir Pouran Ben Veyseh; Franck Dernoncourt; Thien Huu Nguyen; | 2021-01-01 | |
795 | Entity Relation Extraction Based on Entity Indicators IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because a sentence often contains several named entity pairs, a … |
YONGBIN QIN et. al. | Symmetry | 2021-01-01 |
796 | Multimodal Relation Extraction with Efficient Graph Alignment IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) is a fundamental process in constructing knowledge graphs. However, previous methods on relation extraction suffer sharp performance decline in short and … |
CHANGMENG ZHENG et. al. | Proceedings of the 29th ACM International Conference on … | 2021-01-01 |
797 | KB-QA Based on Multi-task Learning and Negative Sample Generation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One popular approach to solve the KB-QA problem is to use a pipeline of several … |
Liao Cheng; Feiyang Xie; Jiangtao Ren; | Inf. Sci. | 2021-01-01 |
798 | Exploring Sentence Embedding Structures for Semantic Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Sentence embeddings encode natural language sentences as low-dimensional, dense vectors and have improved NLP tasks, including relation extraction, which aims at identifying … |
Alexander Kalinowski; Yuan An; | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
799 | Selection Gate-based Networks for Semantic Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Semantic relatedness between context information and entities, which is one of the most easily accessible features, has been proven to be very useful for detecting the semantic … |
XIAJIONG SHEN et. al. | International Journal of Embedded Systems | 2021-01-01 |
800 | Enhanced Prototypical Network for Few-shot Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Most existing methods for relation extraction tasks depend heavily on large-scale annotated data; they cannot learn from existing knowledge and have low generalization ability. It … |
Wen Wen; Yongbin Liu; Chunping Ouyang; Qiang Lin; Tong Lee Chung; | Inf. Process. Manag. | 2021-01-01 |
801 | Clustering-Augmented Multi-instance Learning for Neural Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qi Zhang; Siliang Tang; Jinquan Sun; Yu Wang; Lei Zhang; | 2021-01-01 | |
802 | Multi-granularity Semantic Representation Model for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In natural language, a group of words constitute a phrase and several phrases constitute a sentence. However, existing transformer-based models for sentence-level tasks abstract … |
Ming Lei; Heyan Huang; Chong Feng; | Neural Comput. Appl. | 2021-01-01 |
803 | MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Document-level relation extraction aims to detect the relations within one document, which is challenging since it requires complex reasoning using mentions, entities, local and … |
JINGYE LI et. al. | 2021-01-01 | |
804 | A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The task to extract relations tries to identify relationships between two named entities in a sentence. Because a sentence usually contains several named entities, capturing … |
Jinling Xu; Yanping Chen; Yongbin Qin; Ruizhang Huang; Qinghua Zheng; | Symmetry | 2021-01-01 |
805 | Adverse Drug Reaction Extraction on Electronic Health Records Written in Spanish: A PhD Thesis Overview Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The aim of this work is the automatic extraction of Adverse Drug Reactions (ADRs) in Electronic Health Records (EHRs) written in Spanish. From Natural Language Processing (NLP) … |
Sara Santiso; | IberSPEECH 2021 | 2021-01-01 |
806 | BioSGAN: Protein-Phenotype Co-mention Classification Using Semi-Supervised Generative Adversarial Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Valuable and relevant information that relates human proteins with their phenotypes in biomedical literature stays hidden from biomedical scientists due to the rapid rise in … |
Francis Anokye; Indika Kahanda; | 2021 IEEE 34th International Symposium on Computer-Based … | 2021-01-01 |
807 | Chinese Relation Extraction with Flat-Lattice Encoding and Pretrain-Transfer Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiuyue Zeng; Jiang Zhong; Chen Wang; Cong Hu; | 2021-01-01 | |
808 | Classifier-adaptation Knowledge Distillation Framework for Relation Extraction and Event Detection with Imbalanced Data IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Fundamental information extraction tasks, such as relation extraction and event detection, suffer from a data imbalance problem. To alleviate this problem, existing methods rely … |
Dandan Song; Jing Xu; Jinhui Pang; Heyan Huang; | Inf. Sci. | 2021-01-01 |
809 | Pattern-based Bootstrapping Framework for Biomedical Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The progress made in the realm of ‘-omics’ technologies has led to a tremendous increase in the quantum of biomedical research published. Information extraction from this huge … |
S. S. Deepika; T. V. Geetha; | Eng. Appl. Artif. Intell. | 2021-01-01 |
810 | Biomedical Cross-sentence Relation Extraction Via Multihead Attention and Graph Convolutional Networks IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Most biomedical information extraction efforts are focused on binary relations, there is a strong need to extract drug–gene–mutation n-ary relations among cross-sentences. In … |
DI ZHAO et. al. | Appl. Soft Comput. | 2021-01-01 |
811 | CVAE-based Re-anchoring for Implicit Discourse Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Training implicit discourse relation classifiers suffers from data sparsity. Variational AutoEncoder (VAE) appears to be the proper solution. It is because that VAE is able to … |
Zujun Dou; Yu Hong; Yu Sun; Guodong Zhou; | Conference on Empirical Methods in Natural Language … | 2021-01-01 |
812 | Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Current state-of-the-art joint entity and relation extraction framework is based on span-level entity classification and relation identification between pairs of entity mentions. … |
Haiyang Zhang; Guanqun Zhang; Ricardo Ma; | Applied Sciences | 2021-01-01 |
813 | Enity Relation Extraction of Industrial Robot PHM Based on BiLSTM-CRF and Multi-head Selection Related Papers Related Patents Related Grants Related Venues Related Experts View |
SONGHAI LIN et. al. | 2021-01-01 | |
814 | A Joint Model for Entity and Relation Extraction Based on BERT IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recent years, as the knowledge graph has attained significant achievements in many specific fields, which has become one of the core driving forces for the development of the … |
BO QIAO et. al. | Neural Computing and Applications | 2021-01-01 |
815 | Improving Distant Supervised Relation Extraction with Noise Detection Strategy Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervised relation extraction (DSRE) is widely used to extract novel relational facts from plain text, so as to improve the knowledge graph. However, distant supervision … |
XIAOYAN MENG et. al. | Applied Sciences | 2021-01-01 |
816 | Distant Supervised Relation Extraction with Position Feature Attention and Selective Bag Attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision greatly reduces manual consumption by automatically labeling data. The relation extraction methods under distant supervision divide sentences with the same … |
Jiasheng Wang; Qiongxin Liu; | Neurocomputing | 2021-01-01 |
817 | CyberRel: Joint Entity and Relation Extraction for Cybersecurity Concepts IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
YONGYAN GUO et. al. | 2021-01-01 | |
818 | Weighted Graph Convolution Over Dependency Trees for Nontaxonomic Relation Extraction on Public Opinion Information Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Currently, with the continuous development of relation extraction tasks, we notice that the ability to extract nontaxonomic relations has improved frustratingly slowly, and the … |
Guangyao Wang; Shengquan Liu; Fuyuan Wei; | Applied Intelligence | 2021-01-01 |
819 | Improving Sentence-Level Relation Classification Via Machine Reading Comprehension and Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View |
Bo Xu; Zhengqi Zhang; Xiangsan Zhao; Hui Song; Ming Du; | 2021-01-01 | |
820 | Cost-Effective Memory Replay for Continual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yunong Chen; Yanlong Wen; Haiwei Zhang; | 2021-01-01 | |
821 | Deep Learning System for Biomedical Relation Extraction Combining External Sources of Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View |
Diana Sousa; | 2021-01-01 | |
822 | Towards Learning Terminological Concept Systems from Multilingual Natural Language Text Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Terminological Concept Systems (TCS) provide a means of organizing, structuring and representing domain-specific multilingual information and are important to ensure … |
Lennart Wachowiak; Christian Lang; Barbara Heinisch; Dagmar Gromann; | 2021-01-01 | |
823 | Drug Disease Relation Extraction from Biomedical Literature Using NLP and Machine Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting the relations between medical concepts is very valuable in the medical domain. Scientists need to extract relevant information and semantic relations between medical … |
Wahiba Ben Abdessalem Karaa; Eman H. Alkhammash; Aida Bchir; | Mob. Inf. Syst. | 2021-01-01 |
824 | A Multi-grained Attention Network for Multi-labeled Distant Supervision Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision is an efficient approach to generate labeled data automatically for relation extraction and it is by nature a multi-instance multi-label learning issue. … |
Mingjie Tang; Bo Yang; Hao Xu; | 2021 IEEE 6th International Conference on Computer and … | 2021-01-01 |
825 | Visual Relationship Detection with Region Topology Structure Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual relationship detection is crucial for scene understanding of images, which aims to detect objects in the image and classify the visual relation for each pair of objects. In … |
Le Zhang; Ying Wang; HaiShun Chen; Jie Li; ZhenXi Zhang; | Inf. Sci. | 2021-01-01 |
826 | Learning Relatedness Between Types with Prototypes for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation schemas are often pre-defined for each relation dataset. Relation types can be related from different datasets and have overlapping semantics. We hypothesize we can … |
Lisheng Fu; Ralph Grishman; | 2021-01-01 | |
827 | Improving Relation Extraction Via Joint Coding Using BiLSTM and DCNN Related Papers Related Patents Related Grants Related Venues Related Experts View |
KAIXU WANG et. al. | 2021-01-01 | |
828 | Dependency Parsing-based Entity Relation Extraction Over Chinese Complex Text Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Open Relation Extraction (ORE) plays a significant role in the field of Information Extraction. It breaks the limitation that traditional relation extraction must pre-define … |
Shanshan Qi; Limin Zheng; Feiyu Shang; | 2021-01-01 | |
829 | Dynamic Graph Transformer for Implicit Tag Recognition Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Textual information extraction is a typical research topic in the NLP community. Several NLP tasks such as named entity recognition and relation extraction between entities have … |
Yi-Ting Liou; Chung-Chi Chen; Hen-Hsen Huang; Hsin-Hsi Chen; | 2021-01-01 | |
830 | Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Fine-grained temporal relation extraction (FineTempRel) aims to recognize the durations and timeline of event mentions in text. A missing part in the current deep learning models … |
Minh Tran Phu; Minh Le Nguyen; Thien Huu Nguyen; | WNUT | 2021-01-01 |
831 | Open Relation Extraction for Chinese Noun Phrases IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Extraction (RE) aims at harvesting relational facts from texts. A majority of existing research targets at knowledge acquisition from sentences, where subject-verb-object … |
Chengyu Wang; Xiaofeng He; Aoying Zhou; | IEEE Transactions on Knowledge and Data Engineering | 2021-01-01 |
832 | A General Framework for First Story Detection Utilizing Entities and Their Relations Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: News portals, such as Yahoo News or Google News, collect large amounts of news articles from a variety of sources on a daily basis. Only a small portion of these documents can be … |
Nikolaos Panagiotou; Cem Akkaya; Kostas Tsioutsiouliklis; Vana Kalogeraki; Dimitrios Gunopulos; | IEEE Transactions on Knowledge and Data Engineering | 2021-01-01 |
833 | Entity-Centric Fully Connected GCN for Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is an important task in the field of natural language processing, and it is one of the important steps in constructing a knowledge graph, which can greatly … |
JUN LONG et. al. | Applied Sciences | 2021-01-01 |
834 | Multi Spatial Relation Detection in Images Related Papers Related Patents Related Grants Related Venues Related Experts View |
Brandon Birmingham; Adrian Muscat; | Spatial Cognition & Computation | 2021-01-01 |
835 | Relation Extraction Model Based on Keywords Attention (S) Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yu Chen; | Proceedings of the 33rd International Conference on … | 2021-01-01 |
836 | Multilingual Entity and Relation Extraction Dataset and Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: We present a novel dataset and model for a multilingual setting to approach the task of Joint Entity and Relation Extraction. The SMiLER dataset consists of 1.1 M annotated … |
Alessandro Seganti; Klaudia Firląg; Helena Skowrońska; Michał Satława; Piotr Andruszkiewicz; | 2021-01-01 | |
837 | Named Entity Recognition and Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense … |
Zara Nasar; S. Waqar Jaffry; Muhammad Kamran Malik; | ACM Computing Surveys (CSUR) | 2021-01-01 |
838 | Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) is an essential task in natural language processing. Given a context, RE aims to classify an entity-mention pair into a set of pre-defined relations. In … |
Xian Zhu; Lele Zhang; Jiangnan Du; Zhifeng Xiao; | Applied Sciences | 2021-01-01 |
839 | Improving Distantly-Supervised Relation Extraction Through BERT-Based Label and Instance Embeddings IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distantly-supervised relation extraction (RE) is an effective method to scale RE to large corpora but suffers from noisy labels. Existing approaches try to alleviate noise through … |
Despina Christou; Grigorios Tsoumakas; | IEEE Access | 2021-01-01 |
840 | FSSRE: Fusing Semantic Feature and Syntactic Dependencies Feature for Threat Intelligence Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xuren Wang; | Proceedings of the 33rd International Conference on … | 2021-01-01 |
841 | Relation Extraction Method Based on Two-Way Attentions of Syntactic Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View |
XUEKAI ZHANG et. al. | Advances in Intelligent Automation and Soft Computing | 2021-01-01 |
842 | Relation Extraction with Synthetic Explanations and Neural Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The state-of-the-art for Relation Extraction, defined as the detection of existing relations between a pair of entities in a sentence, relies on neural networks that require a … |
Rozan Chahardoli; Denilson Barbosa; Davood Rafiei; | 2021 International Symposium on Electrical, Electronics and … | 2021-01-01 |
843 | Knowledge Graph Question Answering Based on TE-BiLTM and Knowledge Graph Embedding Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Abstract: Knowledge graph question answering (KGQA) aims to use facts in the knowledge graph to answer natural language questions. Relation extraction, as one of the sub-tasks of … |
JIANBIN LI et. al. | 2021 the 5th International Conference on Innovation in … | 2021-01-01 |
844 | Dependency Tree Positional Encoding Method for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Dependency trees are recently used for relation extraction tasks to capture long-range relations among entities. Many studies have proposed various methods to apply dependency … |
Chunghyeon Cho; Yong Suk Choi; | Proceedings of the 36th Annual ACM Symposium on Applied … | 2021-01-01 |
845 | Merging Web Tables for Relation Extraction with Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jhomara Luzuriaga; Emir Munoz; Henry Rosales-Mendez; Aidan Hogan; | IEEE Transactions on Knowledge and Data Engineering | 2021-01-01 |
846 | KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision … |
Ningyi Mao; Wenti Huang; Hai Zhong; | Applied Sciences | 2021-01-01 |
847 | LAPREL: A Label-Aware Parallel Network for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a crucial task in natural language processing (NLP) that aims to extract all relational triples from a given sentence. Extracting overlapping relational … |
Xiang Li; Junan Yang; Pengjiang Hu; Hui Liu; | Symmetry | 2021-01-01 |
848 | Attention Guided Relation Detection Approach for Video Visual Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qianwen Cao; Heyan Huang; | IEEE Transactions on Multimedia | 2021-01-01 |
849 | Adversarial Learning with Domain-Adaptive Pretraining for Few-Shot Relation Classification Across Domains Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The existing methods for domain-adaptive few-shot relation classification based on word embeddings or pretraining models trained on massive corpora, are not strong enough to cover … |
Wen Qian; Yuesheng Zhu; | 2021 IEEE 6th International Conference on Computer and … | 2021-01-01 |
850 | Predicting Informativeness of Semantic Triples Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Many automatic semantic relation extraction tools extract subject-predicate-object triples from unstructured text. However, a large quantity of these triples merely represent … |
Judita Preiss; | 2021-01-01 | |
851 | Interpretability Rules: Jointly Bootstrapping A Neural Relation Extractorwith An Explanation Decoder Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We introduce a method that transforms a rule-based relation extraction (RE) classifier into a neural one such that both interpretability and performance are achieved. Our approach … |
Zheng Tang; Mihai Surdeanu; | 2021-01-01 | |
852 | Active Learning for Interactive Relation Extraction in A French Newspaper’s Articles Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a subtask of natural langage processing that has seen many improvements in recent years, with the advent of complex pre-trained architectures. Many of these … |
Cyrielle Mallart; Michel Le Nouy; G. Gravier; P. Sébillot; | Recent Advances in Natural Language Processing | 2021-01-01 |
853 | Piecewise Convolutional Neural Networks with Position Attention and Similar Bag Attention for Distant Supervision Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Weijiang Li; Qing Wang; Jiekun Wu; Zhengtao Yu; | Applied Intelligence | 2021-01-01 |
854 | Systematic Analysis of Joint Entity and Relation Extraction Models in Identifying Overlapping Relations Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yuchen Luo; Zhenjie Huang; Kai Zheng; Tianyong Hao; | Neural Computing for Advanced Applications | 2021-01-01 |
855 | BiLSTM Embedding Pretraining for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Many recent relation extraction methods are proposed based on distantly supervised learning to address the issue of expensive human labelling. They automatically align relation … |
Haojie Huang; Raymond K. Wong; | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
856 | Multi-Stream Semantics-Guided Dynamic Aggregation Graph Convolution Networks to Extract Overlapping Relations Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The existing relation extraction approaches select the relevant partial dependency structures and exhibit the limitation associated with long-distance dependencies. Moreover, due … |
Xiushan Liu; Jun Cheng; Qin Zhang; | IEEE Access | 2021-01-01 |
857 | Multi-granularity Sequential Neural Network for Document-level Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiaofeng Liu; Kaiwen Tan; Shoubin Dong; | Inf. Process. Manag. | 2021-01-01 |
858 | Near-Perfect Relation Extraction from Family Books Related Papers Related Patents Related Grants Related Venues Related Experts View |
George Nagy; | 2021-01-01 | |
859 | Improving Relation Extraction Beyond Sentence Boundaries Using Attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Reation Extraction(RE) is the subprocess of Information Extraction(IE) which focuses on determining and extracting the reation between two participating entities. Most of the past … |
C. A. Deepa; P. C. Reghu Raj; Ajeesh Ramanujan; | 2021-01-01 | |
860 | Joint Entity and Relation Extraction for Long Text Related Papers Related Patents Related Grants Related Venues Related Experts View |
Dong Cheng; Hui Song; Xianglong He; Bo Xu; | 2021-01-01 | |
861 | Chinese Relation Extraction Using Extend Softword Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recent years, many scholars have chosen to use word lexicons to incorporate word information into a model based on character input to improve the performance of Chinese … |
Bo Kong; Shengquan Liu; Fuyuan Wei; Liruizhi Jia; Guangyao Wang; | IEEE Access | 2021-01-01 |
862 | Multilevel Entity-Informed Business Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hadjer Khaldi; Farah Benamara; Amine Abdaoui; Nathalie Aussenac-Gilles; EunBee Kang; | 2021-01-01 | |
863 | Reinforced Natural Language Inference for Distantly Supervised Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
Bo Xu; Xiangsan Zhao; Chaofeng Sha; Minjun Zhang; Hui Song; | 2021-01-01 | |
864 | Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
TIANCHI YANG et. al. | 2021-01-01 | |
865 | Incorporating Syntactic Information Into Relation Representations for Enhanced Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Li Cui; Deqing Yang; Jiayang Cheng; Yanghua Xiao; | 2021-01-01 | |
866 | SaGCN: Structure-Aware Graph Convolution Network for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
SHUANGJI YANG et. al. | 2021-01-01 | |
867 | Open Relation Extraction in Patent Claims with A Hybrid Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Research on relation extraction from patent documents, a high-priority topic of natural language process in recent years, is of great significance to a series of patent downstream … |
Boting Geng; | Wirel. Commun. Mob. Comput. | 2021-01-01 |
868 | Machine Learning Algorithm for Information Extraction from Gynaecological Domain in Tamil Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Information Extraction is a significant task in Natural Language Processing. It is the process of extracting useful information from unstructured text. Information extraction … |
M. Rajasekar; Angelina Geetha; | 2021-01-01 | |
869 | VidVRD 2021: The Third Grand Challenge on Video Relation Detection IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: ACM Multimedia 2021 Video Relation Understanding Challenge is the third grand challenge which aims at exploring the relationship of subjects and objects appearing in videos for … |
WEI JI et. al. | Proceedings of the 29th ACM International Conference on … | 2021-01-01 |
870 | Chinese Medical Relation Extraction Based on Multi-hop Self-attention Mechanism IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The medical literature is the most important way to demonstrate academic achievements and academic exchanges. Massive medical literature has become a huge treasure trove of … |
TONGXUAN ZHANG et. al. | Int. J. Mach. Learn. Cybern. | 2021-01-01 |
871 | Constructing Knowledge Graphs for Online Collaborative Programming Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This study aimed to automatically construct knowledge graphs for online collaborative programming. We proposed several models and developed a system to construct knowledge graphs … |
Yuanyi Zhen; Lanqin Zheng; Penghe Chen; | IEEE Access | 2021-01-01 |
872 | Exploit A Multi-head Reference Graph for Semi-supervised Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Manual annotation of labeled data for relation extraction is time-consuming and labor-intensive. Semi-supervised methods can offer helping hands for this problem and have aroused … |
WANLI LI et. al. | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
873 | Conflict Resolution Using Relation Classification: High-level Data Fusion in Data Integration Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The aim of conflict resolution in data integration systems is to identify the true values from among different and conflicting claims about a single entity provided by different … |
Zeinab Nakhaei; Ali Ahmadi; Arash Sharifi; Kambiz Badie; | Comput. Sci. Inf. Syst. | 2021-01-01 |
874 | Semantic Neural Network Ensemble for Automated Dependency Relation Extraction from Bridge Inspection Reports Related Papers Related Patents Related Grants Related Venues Related Experts View |
Kaijian Liu; Nora El-Gohary; | Journal of Computing in Civil Engineering | 2021-01-01 |
875 | ATFE: A Two-dimensional Feature Encoding-based Sentence-level Attention Model for Distant Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Shiyang Li; | Proceedings of the 33rd International Conference on … | 2021-01-01 |
876 | Neural Relation Extraction: A Review Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. In this study, we make a clear categorization of the … |
Mehmet Aydar; Özge Bozal; Furkan Özbay; | Turkish J. Electr. Eng. Comput. Sci. | 2021-01-01 |
877 | EntityBERT: Entity-centric Masking Strategy for Model Pretraining for The Clinical Domain Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Transformer-based neural language models have led to breakthroughs for a variety of natural language processing (NLP) tasks. However, most models are pretrained on general domain … |
Chen Lin; Timothy Miller; Dmitriy Dligach; Steven Bethard; Guergana Savova; | 2021-01-01 | |
878 | Illation of Video Visual Relation Detection Based on Graph Neural Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Visual relation detection task is the bridge between semantic text and image information, and it can better express the content of images or video through the relation triple . … |
Mingcheng Qu; Jianxun Cui; Yuxi Nie; Tonghua Su; | IEEE Access | 2021-01-01 |
879 | Densely Connected Graph Attention Network Based on Iterative Path Reasoning for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hongya Zhang; Zhen Huang; Zhenzhen Li; Dongsheng Li; Feng Liu; | 2021-01-01 | |
880 | MNRE: A Challenge Multimodal Dataset for Neural Relation Extraction with Visual Evidence in Social Media Posts IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting relations in social media posts is challenging when sentences lack of contexts. However, images related to these sentences can supplement such missing contexts and help … |
Changmeng Zheng; Zhiwei Wu; Junhao Feng; Ze Fu; Yi Cai; | 2021 IEEE International Conference on Multimedia and Expo … | 2021-01-01 |
881 | Discourse-Based Sentence Splitting Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Sentence splitting involves the segmentation of a sentence into two or more shorter sentences. It is a key component of sentence simplification, has been shown to help human … |
Liam Cripwell; Joël Legrand; Claire Gardent; | Conference on Empirical Methods in Natural Language … | 2021-01-01 |
882 | CovidBERT-Biomedical Relation Extraction for Covid-19 IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Given the ongoing pandemic of Covid-19 which has had a devastating impact on society and the economy, and the explosive growth of biomedical literature, there has been a growing … |
Shashank Hebbar; Ying Xie; | 2021-01-01 | |
883 | Attention-based Seq2seq Regularisation for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is an important task for information extraction aiming to detect and extract semantic relationships between entity pairs in sentences. A lot of recently … |
Haojie Huang; Raymond K. Wong; | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
884 | Unsupervised Relation Extraction Using Sentence Encoding Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction between two named entities from unstructured text is an important natural language processing task. In the absence of labelled data, semi-supervised and … |
Manzoor Ali; Muhammad Saleem; Axel-Cyrille Ngonga Ngomo; | 2021-01-01 | |
885 | Multilingual Epidemic Event Extraction : From Simple Classification Methods to Open Information Extraction (OIE) and Ontology Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: There is an incredible amount of information available in the form of textual documents due to the growth of information sources. In order to get the information into an … |
Sihem Sahnoun; Gaël Lejeune; | 2021-01-01 | |
886 | A Graph Convolutional Network With Multiple Dependency Representations for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Dependency analysis can assist neural networks to capture semantic features within a sentence for entity relation extraction (RE). Both hard and soft strategies of encoding … |
YANFENG HU et. al. | IEEE Access | 2021-01-01 |
887 | A Method of Relation Extraction: Integrating Graph Convolutional Networks, Relative Entity Position Attention and Back-Multi-Head-Attention Mechanism Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is the task of identifying entity pairs and relationship between entity pairs. Moreover, it is a key method for knowledge discovery. It has been proven … |
Ci Liu; Xuetong Zhao; Yawei Zhao; | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
888 | Learning Context-Aware Convolutional Filters for Implicit Discourse Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Implicit discourse relation classification (IDRC) is considered the most difficult component of shallow discourse parsing as the relation prediction in the absence of necessary … |
Kashif Munir; Hai Zhao; Zuchao Li; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2021-01-01 |
889 | Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The number of biomedical documents is increasing rapidly. Accordingly, a demand for extracting knowledge from large-scale biomedical texts is also increasing. BERT-based models … |
Satoshi Hiai; Kazutaka Shimada; Taiki Watanabe; Akiva Miura; Tomoya Iwakura; | 2021-01-01 | |
890 | Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, … |
ACM Transactions on Intelligent Systems and Technology | 2021-01-01 | |
891 | Application of Knowledge-oriented Convolutional Neural Network For Causal Relation Extraction In South China Sea Conflict Issues Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Online news articles are an important source of information for decisions makers to understand the causal relation of events that happened. However, understanding the causality of … |
Koh Leong Chien; Anazida Zainal; Fuad A. Ghaleb; Mohd Nizam Kassim; | 2021 3rd International Cyber Resilience Conference (CRC) | 2021-01-01 |
892 | Contrastive Information Extraction With Generative Transformer IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Information extraction tasks such as entity relation extraction and event extraction are of great importance for natural language processing and knowledge graph construction. In … |
NINGYU ZHANG et. al. | IEEE/ACM Transactions on Audio, Speech, and Language … | 2021-01-01 |
893 | Document-level Relation Extraction Using Evidence Reasoning on RST-GRAPH IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
HAILIN WANG et. al. | Knowl. Based Syst. | 2021-01-01 |
894 | GCN2-NAA: Two-stage Graph Convolutional Networks with Node-Aware Attention for Joint Entity and Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint extraction of entities and relations is critical for many tasks of Natural Language Processing (NLP), which aims to extract all triplets in the text. However, the huge … |
WeiCai Niu; Quan Chen; Weiwen Zhang; Jianwen Ma; Zhongqiang Hu; | 2021 13th International Conference on Machine Learning and … | 2021-01-01 |
895 | TEBC-Net: An Effective Relation Extraction Approach for Simple Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jianbin Li; Ketong Qu; Jingchen Yan; Liting Zhou; Long Cheng; | 2021-01-01 | |
896 | Document-level Relation Extraction Via Graph Transformer Networks and Temporal Convolutional Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yong Shi; Yang Xiao; Pei Quan; Minglong Lei; Lingfeng Niu; | Pattern Recognit. Lett. | 2021-01-01 |
897 | Enhancing Dialogue-based Relation Extraction By Speaker and Trigger Words Prediction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Identifying relations from dialogues is more challenging than traditional sentence-level relation extraction (RE), since the difficulties of speaker information representation and … |
Tianyang Zhao; Zhao Yan; Yunbo Cao; Zhoujun Li; | 2021-01-01 | |
898 | Relation Extraction: A Brief Survey on Deep Neural Network Based Methods Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge, data, algorithms and computing power are the foundations of artificial intelligence (AI), for which knowledge is the most powerful support. An effective way to acquire … |
Hailin Wang; Guoming Lu; Jin Yin; Ke Qin; | 2021 The 4th International Conference on Software … | 2021-01-01 |
899 | Hate Speech Detection in Asian Languages:A Survey Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this study, we present a language-based survey of hate speech detection in Asian languages. The motivation of this survey is to encourage the development of an automated hate … |
L K Dhanya; Kannan Balakrishnan; | 2021 International Conference on Communication, Control and … | 2021-01-01 |
900 | WIND: Weighting Instances Differentially for Model-Agnostic Domain Adaptation Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Domain Adaptation is a fundamental problem in machine learning and natural language processing. In this paper, we study the domain adaptation problem from the perspective of … |
Xiang Chen; Yue Cao; Xiaojun Wan; | 2021-01-01 | |
901 | Distant Supervision for Relation Extraction Via Noise Filtering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As a widely used method in relation extraction at the present stage suggests, distant supervision is affected by label noise. The data noise is introduced artificially due to the … |
Jing Chen; Zhiqiang Guo; Jie Yang; | 2021 13th International Conference on Machine Learning and … | 2021-01-01 |
902 | Relation Extraction with Type-aware Map Memories of Word Dependencies Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation extraction is an important task in information extraction and retrieval that aims to extract relations among the given entities from running texts. To achieve a good … |
Guimin Chen; Yuanhe Tian; Yan Song; Xiang Wan; | 2021-01-01 | |
903 | A Region-based Hypergraph Network for Joint Entity-relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qian Wan; Luona Wei; Xinhai Chen; Jie Liu; | Knowl. Based Syst. | 2021-01-01 |
904 | What Are They Talking About? Relation Extraction from News to Identify Research Directions in Emerging Technologies Related Papers Related Patents Related Grants Related Venues Related Experts View |
Nicolas Prat; | 2021-01-01 | |
905 | GrantRel: Grant Information Extraction Via Joint Entity and Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: August 1–6, 2021. ©2021 Association for Computational Linguistics 2674 GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction Junyi Bian 1 8 , Li Huang 1 … |
Junyi Bian; Li Huang; Xiaodi Huang; Hong Zhou; Shanfeng Zhu; | 2021-01-01 | |
906 | Parasitic Network: Zero-Shot Relation Extraction for Knowledge Graph Populating Related Papers Related Patents Related Grants Related Venues Related Experts View |
SHENGBIN JIA et. al. | 2021-01-01 | |
907 | HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. Current RE approaches achieve fantastic results on common … |
QIAO CHENG et. al. | 2021-01-01 | |
908 | Paths to Relation Extraction Through Semantic Structure IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Syntactic and semantic structure directly reflect relations expressed by the text at hand and are thus very useful for the relation extraction (RE) task. Their symbolic nature … |
Jonathan Yellin; Omri Abend; | 2021-01-01 | |
909 | Relating Relations: Meta-Relation Extraction from Online Health Forum Posts Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a key task in knowledge extraction, and is commonly defined as the task of identifying relations that hold between entities in text. This thesis proposal … |
Daniel Stickley; | 2021-01-01 | |
910 | Relation Extraction in Dialogues: A Deep Learning Model Based on The Generality and Specialty of Dialogue Text IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction from dialogue text is an innovative task in natural language processing. In addition to the general characteristics of general relation extraction from news or … |
Mengjia Zhou; Donghong Ji; Fei Li; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2021-01-01 |
911 | REBEL: Relation Extraction By End-to-end Language Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View |
Pere-Lluís Huguet Cabot; Roberto Navigli; | 2021-01-01 | |
912 | Disease-Symptom Relation Extraction from Medical Text Corpora with BERT Related Papers Related Patents Related Grants Related Venues Related Experts View |
Adrian Schiegl; | 2021-01-01 | |
913 | Selection Gate-based Networks for Semantic Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
JUN SUN et. al. | Int. J. Embed. Syst. | 2021-01-01 |
914 | From What to Why: Improving Relation Extraction with Rationale Graph Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Which type of information affects the existing neural relation extraction (RE) models to make correct decisions is an important question. In this paper, we observe that entity … |
ZHENYU ZHANG et. al. | 2021-01-01 | |
915 | On Robustness and Bias Analysis Of BERT-Based Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks. However, the resultant model generalizability remains … |
LUOQIU LI et. al. | Knowledge Graph and Semantic Computing: Knowledge Graph … | 2021-01-01 |
916 | Joint Extraction of Entities and Relations Via An Entity Correlated Attention Neural Model IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Named entity recognition and relation extraction are crucial tasks in natural language processing . As the traditional pipelined manners may suffer from the error propagation … |
REN LI et. al. | Information Sciences | 2021-01-01 |
917 | Improved Distant Supervision Relation Extraction Based on Edge-reasoning Hybrid Graph Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision relation extraction (DSRE) trains a classifier by automatically labeling data through aligning triples in the knowledge base (KB) with large-scale corpora. … |
Shirong Shen; Shangfu Duan; Huan Gao; Guilin Qi; | J. Web Semant. | 2021-01-01 |
918 | Deep Learning on Graphs for Natural Language Processing Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to … |
Lingfei Wu; Yu Chen; Heng Ji; Yunyao Li; | 2021-01-01 | |
919 | Question Answering System Using Knowledge Graph Generation and Knowledge Base Enrichment with Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
K. Sathees Kumar; S. Chitrakala; | 2021-01-01 | |
920 | Distant Supervision for Relation Extraction with Sentence Selection and Interaction Representation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision (DS) has been widely used for relation extraction (RE), which automatically generates large-scale labeled data. However, there is a wrong labeling problem, … |
Tiantian Chen; Nianbin Wang; Hongbin Wang; Haomin Zhan; | Wirel. Commun. Mob. Comput. | 2021-01-01 |
921 | A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
KUN DING et. al. | Cmc-computers Materials & Continua | 2021-01-01 |
922 | Image-Based Relation Classification Approach for Table Structure Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View |
Koji Ichikawa; | 2021-01-01 | |
923 | Auto-learning Convolution-Based Graph Convolutional Network for Medical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
MENGYUAN QIAN et. al. | 2021-01-01 | |
924 | Entity and Entity Type Enhanced Capsule Network for Distant Supervision Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In the task of distant supervision relation extraction, attention mechanism is introduced to distinguish correct instances from noise. However, the existing attention is often … |
Hongjun Heng; Renjie Li; | 2021 International Joint Conference on Neural Networks … | 2021-01-01 |
925 | Deep Semantic Fusion Representation Based on Special Mechanism of Information Transmission for Joint Entity-Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Wenqiang Xu; Shiqun Yin; Junfeng Zhao; Ting Pu; | 2021-01-01 | |
926 | A Noise-aware Method with Type Constraint Pattern for Neural Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jianfeng Qu; Wen Hua; Dantong Ouyang; Xiaofang Zhou; | IEEE Transactions on Knowledge and Data Engineering | 2021-01-01 |
927 | NA-Aware Machine Reading Comprehension for Document-Level Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction aims to identify semantic relations between target entities from the document. Most of the existing work roughly treats the document as a long … |
Zhenyu Zhang; Bowen Yu; Xiaobo Shu; Tingwen Liu; | 2021-01-01 | |
928 | Distilling The Documents for Relation Extraction By Topic Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View |
Minghui Wang; Ping Xue; Ying Li; Zhonghai Wu; | 2021-01-01 | |
929 | Interventional Video Relation Detection IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Video Visual Relation Detection (VidVRD) aims to semantically describe the dynamic interactions across visual concepts localized in a video in the form of subject, predicate, … |
Yicong Li; Xun Yang; Xindi Shang; Tat-Seng Chua; | Proceedings of the 29th ACM International Conference on … | 2021-01-01 |
930 | MRE: A Military Relation Extraction Model Based on BiGRU and Multi-Head Attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for … |
YIWEI LU et. al. | Symmetry | 2021-01-01 |
931 | A Boundary Determined Neural Model for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Existing models extract entity relations only after two entity spans have been precisely extracted that influenced the performance of relation extraction. Compared with … |
RUI XUE TANG; | INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL | 2021-01-01 |
932 | Utilizing Graph Neural Networks to Improving Dialogue-based Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction has been an active research interest in the field of Natural Language Processing (NLP). The past works primarily focused on a corpus of formal text which is … |
Lulu Zhao; Weiran Xu; Sheng Gao; Jun Guo; | Neurocomputing | 2021-01-01 |
933 | Targeted BERT Pre-training and Fine-Tuning Approach for Entity Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity relation extraction (ERE) is an important task in the field of information extraction. With the wide application of pre-training language model (PLM) in natural language … |
Chao Li; Zhao Qiu; | Communications in Computer and Information Science | 2021-01-01 |
934 | Relation Classification Based on Vietnamese Covid-19 Information Using BERT Model with Typed Entity Markers Related Papers Related Patents Related Grants Related Venues Related Experts View |
Truong Minh Giang; Phan Duy Hung; | 2021-01-01 | |
935 | Double Multi-Head Attention-Based Capsule Network for Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Semantic relation classification is an important task in the field of nature language processing. The existing neural network relation classification models introduce attention … |
Hongjun Heng; Renjie Li; | 2021-01-01 | |
936 | Reinforcement Learning with Dual Attention Guided Graph Convolution for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: To better learn the dependency relationship between nodes, we address the relationship extraction task by capturing rich contextual dependencies based on the attention mechanism, … |
Zhixin Li; Yaru Sun; Suqin Tang; Canlong Zhang; Huifang Ma; | 2020 25th International Conference on Pattern Recognition … | 2021-01-01 |
937 | Discourse-level Relation Extraction Via Graph Pooling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we leverage the idea of graph pooling and propose to use pooling-unpooling framework on DRE tasks. |
I-Hung Hsu; Xiao Guo; Premkumar Natarajan; Nanyun Peng; | arxiv-cs.CL | 2020-12-31 |
938 | An Embarrassingly Simple Model for Dialogue Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple yet effective model named SimpleRE for the RE task. |
Fuzhao Xue; Aixin Sun; Hao Zhang; Jinjie Ni; Eng Siong Chng; | arxiv-cs.CL | 2020-12-27 |
939 | An Empirical Study of Using Pre-trained BERT Models for Vietnamese Relation Extraction Task at VLSP 2020 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present an empirical study of using pre-trained BERT models for the relation extraction task at the VLSP 2020 Evaluation Campaign. |
Pham Quang Nhat Minh; | arxiv-cs.CL | 2020-12-18 |
940 | Regularized Attentive Capsule Network For Overlapped Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this problem, we propose a novel Regularized Attentive Capsule Network (RA-CapNet) to better identify highly overlapped relations in each informal sentence. |
Tianyi Liu; Xiangyu Lin; Weijia Jia; Mingliang Zhou; Wei Zhao; | arxiv-cs.CL | 2020-12-18 |
941 | R$^2$-Net: Relation Of Relation Learning Network For Sentence Semantic Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. |
KUN ZHANG et. al. | arxiv-cs.CL | 2020-12-16 |
942 | InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources For Distantly-supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce two widely-existing sources in knowledge bases, namely entity descriptions, and multi-grained entity types to enrich the distantly supervised data. |
Zhendong Chu; Haiyun Jiang; Yanghua Xiao; Wei Wang; | arxiv-cs.CL | 2020-12-16 |
943 | Complex Relation Extraction: Challenges And Opportunities IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Then we summarize the existing complex relation extraction tasks and present the definition, recent progress, challenges and opportunities for each task. |
HAIYUN JIANG et. al. | arxiv-cs.CL | 2020-12-08 |
944 | Improving Relation Extraction By Leveraging Knowledge Graph Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we leverage this insight by proposing a multi-task learning approach that improves the performance of RE models by jointly training on RE and KGLP tasks. |
George Stoica; Emmanouil Antonios Platanios; Barnabás Póczos; | arxiv-cs.CL | 2020-12-08 |
945 | From Bag Of Sentences To Document: Distantly Supervised Relation Extraction Via Machine Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new DS paradigm–document-based distant supervision, which models relation extraction as a document-based machine reading comprehension (MRC) task. |
Lingyong Yan; Xianpei Han; Le Sun; Fangchao Liu; Ning Bian; | arxiv-cs.CL | 2020-12-08 |
946 | H-FND: Hierarchical False-Negative Denoising For Distant Supervision Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We here propose H-FND, a hierarchical false-negative denoising framework for robust distant supervision relation extraction, as an FN denoising solution. |
Jhih-Wei Chen; Tsu-Jui Fu; Chen-Kang Lee; Wei-Yun Ma; | arxiv-cs.CL | 2020-12-07 |
947 | Coarse-to-Fine Entity Representations for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, to obtain more comprehensive entity representations, we propose the Coarse-to-Fine Entity Representation model (CFER) that adopts a coarse-to-fine strategy involving two phases. |
Damai Dai; Jing Ren; Shuang Zeng; Baobao Chang; Zhifang Sui; | arxiv-cs.CL | 2020-12-04 |
948 | Event Guided Denoising For Multilingual Relation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. |
Amith Ananthram; Emily Allaway; Kathleen McKeown; | arxiv-cs.CL | 2020-12-04 |
949 | Financial Document Causality Detection Shared Task (FinCausal 2020) IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. |
DOMINIQUE MARIKO et. al. | arxiv-cs.CL | 2020-12-04 |
950 | Rel3D: A Minimally Contrastive Benchmark For Grounding Spatial Relations In 3D IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we fill this gap by constructing Rel3D: the first large-scale, human-annotated dataset for grounding spatial relations in 3D. |
Ankit Goyal; Kaiyu Yang; Dawei Yang; Jia Deng; | arxiv-cs.CV | 2020-12-02 |
951 | Automatic Extraction Of Ranked SNP-Phenotype Associations From Literature Through Detecting Neural Candidates, Negation And Modality Markers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, first a relation extraction method relying on linguistic-based negation detection and neutral candidates is proposed. |
Behrouz Bokharaeian; Alberto Diaz; | arxiv-cs.CL | 2020-12-01 |
952 | Joint Extraction Of Entity And Relation With Information Redundancy Elimination Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve the problem of redundant information and overlapping relations of the entity and relation extraction model, we propose a joint extraction model. |
Yuanhao Shen; Jungang Han; | arxiv-cs.CL | 2020-11-27 |
953 | Learning Relation Prototype From Unlabeled Texts For Long-tail Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a general approach to learn relation prototypesfrom unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient trainingdata. |
YIXIN CAO et. al. | arxiv-cs.CL | 2020-11-27 |
954 | Experiments On Transfer Learning Architectures For Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we explore various BERT-based architectures and transfer learning strategies (i.e., frozen or fine-tuned) for the task of biomedical RE on two corpora. |
Walid Hafiane; Joel Legrand; Yannick Toussaint; Adrien Coulet; | arxiv-cs.CL | 2020-11-24 |
955 | RTFN: A Robust Temporal Feature Network For Time Series Classification IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel robust temporal feature network (RTFN) for feature extraction in time series classification, containing a temporal feature network (TFN) and an LSTM-based attention network (LSTMaN). |
ZHIWEN XIAO et. al. | arxiv-cs.LG | 2020-11-23 |
956 | Dual Supervision Framework For Relation Extraction With Distant Supervision And Human Annotation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To take advantage of the high accuracy of human annotation and the cheap cost of distant supervision, we propose the dual supervision framework which effectively utilizes both types of data. |
Woohwan Jung; Kyuseok Shim; | arxiv-cs.CL | 2020-11-23 |
957 | Learning Informative Representations Of Biomedical Relations With Latent Variable Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this issue, we propose a latent variable model with an arbitrarily flexible distribution to represent the relation between an entity pair. |
Harshil Shah; Julien Fauqueur; | arxiv-cs.CL | 2020-11-20 |
958 | Relation Extraction With Contextualized Relation Embedding (CRE) Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes an architecture for the relation extraction task that integrates semantic information with knowledge base modeling in a novel manner. |
Xiaoyu Chen; Rohan Badlani; | arxiv-cs.CL | 2020-11-19 |
959 | Entity Recognition And Relation Extraction From Scientific And Technical Texts In Russian Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, several modifications of methods for the Russian language are proposed. In this paper, we present a corpus of scientific texts in Russian, RuSERRC. |
Elena Bruches; Alexey Pauls; Tatiana Batura; Vladimir Isachenko; | arxiv-cs.CL | 2020-11-19 |
960 | Within-Between Lexical Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the novel \textit{Within-Between} Relation model for recognizing lexical-semantic relations between words. |
Oren Barkan; Avi Caciularu; Ido Dagan; | emnlp | 2020-11-12 |
961 | AutoPrompt: Eliciting Knowledge From Language Models With Automatically Generated Prompts IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this, we develop AutoPrompt, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided search. |
Taylor Shin; Yasaman Razeghi; Robert L. Logan IV; Eric Wallace; Sameer Singh; | emnlp | 2020-11-12 |
962 | Domain Knowledge Empowered Structured Neural Net For End-to-End Event Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address these issues, we propose a framework that enhances deep neural network with distributional constraints constructed by probabilistic domain knowledge. |
Rujun Han; Yichao Zhou; Nanyun Peng; | emnlp | 2020-11-12 |
963 | Denoising Relation Extraction From Document-level Distant Supervision IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks. |
CHAOJUN XIAO et. al. | emnlp | 2020-11-12 |
964 | FedED: Federated Learning Via Ensemble Distillation For Medical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged. |
DIANBO SUI et. al. | emnlp | 2020-11-12 |
965 | Global-to-Local Neural Networks For Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations. |
Difeng Wang; Wei Hu; Ermei Cao; Weijian Sun; | emnlp | 2020-11-12 |
966 | Recurrent Interaction Network For Jointly Extracting Entities And Classifying Relations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a solution, we design a multi-task learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features for classification. |
Kai Sun; Richong Zhang; Samuel Mensah; Yongyi Mao; Xudong Liu; | emnlp | 2020-11-12 |
967 | An Empirical Study Of Pre-trained Transformers For Arabic Information Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. |
Wuwei Lan; Yang Chen; Wei Xu; Alan Ritter; | emnlp | 2020-11-12 |
968 | Pre-training Entity Relation Encoder With Intra-span And Inter-span Information IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we integrate span-related information into pre-trained encoder for entity relation extraction task. |
YIJUN WANG et. al. | emnlp | 2020-11-12 |
969 | Exposing Shallow Heuristics Of Relation Extraction Models With Challenge Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We identify failure modes of SOTA relation extraction (RE) models trained on TACRED, which we attribute to limitations in the data annotation process. |
Shachar Rosenman; Alon Jacovi; Yoav Goldberg; | emnlp | 2020-11-12 |
970 | Let’s Stop Incorrect Comparisons In End-to-end Relation Extraction! Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we first identify several patterns of invalid comparisons in published papers and describe them to avoid their propagation. We then propose a small empirical study to quantify the most common mistake’s impact and evaluate it leads to overestimating the final RE performance by around 5% on ACE05. |
Bruno Taillé; Vincent Guigue; Geoffrey Scoutheeten; Patrick Gallinari; | emnlp | 2020-11-12 |
971 | SelfORE: Self-supervised Relational Feature Learning For Open Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. |
Xuming Hu; Lijie Wen; Yusong Xu; Chenwei Zhang; Philip Yu; | emnlp | 2020-11-12 |
972 | Two Are Better Than One: Joint Entity And Relation Extraction With Table-Sequence Encoders IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose the novel table-sequence encoders where two different encoders – a table encoder and a sequence encoder are designed to help each other in the representation learning process. |
Jue Wang; Wei Lu; | emnlp | 2020-11-12 |
973 | APE: Argument Pair Extraction From Peer Review And Rebuttal Via Multi-task Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a new argument pair extraction (APE) task on peer review and rebuttal in order to study the contents, the structure and the connections between them. |
Liying Cheng; Lidong Bing; Qian Yu; Wei Lu; Luo Si; | emnlp | 2020-11-12 |
974 | Double Graph Based Reasoning For Document-level Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Graph Aggregation-and-Inference Network (GAIN), a method to recognize such relations for long paragraphs. |
Shuang Zeng; Runxin Xu; Baobao Chang; Lei Li; | emnlp | 2020-11-12 |
975 | Biomedical Information Extraction For Disease Gene Prioritization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a biomedical information extraction (IE) pipeline that extracts biological relationships from text and demonstrate that its components, such as named entity recognition (NER) and relation extraction (RE), outperform state-of-the-art in BioNLP. |
Jupinder Parmar; William Koehler; Martin Bringmann; Katharina Sophia Volz; Berk Kapicioglu; | arxiv-cs.LG | 2020-11-10 |
976 | Unsupervised Relation Extraction From Language Models Using Constrained Cloze Completion IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce RE-Flex, a simple framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction. |
Ankur Goswami; Akshata Bhat; Hadar Ohana; Theodoros Rekatsinas; | emnlp | 2020-11-10 |
977 | The RELX Dataset And Matching The Multilingual Blanks For Cross-lingual Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For evaluation, we introduce a new public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish, called RELX. |
Abdullatif Köksal; Arzucan Özgür; | emnlp | 2020-11-10 |
978 | The Dots Have Their Values: Exploiting The Node-Edge Connections In Graph-based Neural Models For Document-level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this issue, we propose to explicitly compute the representations for the nodes in the graph-based edge-oriented model for DRE. |
Hieu Minh Tran; Minh Trung Nguyen; Thien Huu Nguyen; | emnlp | 2020-11-10 |
979 | Active Testing: An Unbiased Evaluation Method For Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate this problem, we propose a novel evaluation method named active testing through utilizing both the noisy test set and a few manual annotations. |
Pengshuai Li; Xinsong Zhang; Weijia Jia; Wei Zhao; | emnlp | 2020-11-10 |
980 | Minimize Exposure Bias Of Seq2Seq Models In Joint Entity And Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets. |
RANRAN HAORAN ZHANG et. al. | emnlp | 2020-11-10 |
981 | CODER: Knowledge Infused Cross-lingual Medical Term Embedding for Term Normalization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes CODER: contrastive learning on knowledge graphs for cross-lingual medical term representation. |
ZHENG YUAN et. al. | arxiv-cs.CL | 2020-11-05 |
982 | Joint Entity And Relation Extraction With Set Prediction Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To solve this set prediction problem, we propose networks featured by transformers with non-autoregressive parallel decoding. |
DIANBO SUI et. al. | arxiv-cs.CL | 2020-11-03 |
983 | Relation Extraction with Contextualized Relation Embedding Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This submission is a paper that proposes an architecture for the relation extraction task which integrates semantic information with knowledge base modeling in a novel manner. … |
Xiaoyu Chen; Rohan Badlani; | ArXiv | 2020-11-01 |
984 | Argument Pair Extraction from Peer Review and Rebuttal Via Multi-task Learning IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments. However, few works study both of them … |
Liying Cheng; Lidong Bing; Qian Yu; Wei Lu; Luo Si; | Conference on Empirical Methods in Natural Language … | 2020-11-01 |
985 | Within-Between Lexical Relation Classification Using Path-based and Distributional Data Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We propose the novel Within-Between Relation model for recognizing lexical-semantic relations between words. Our model integrates relational and distributional signals, forming an … |
Oren Barkan; Avi Caciularu; Ido Dagan; | Conference on Empirical Methods in Natural Language … | 2020-11-01 |
986 | Investigation Of BERT Model On Biomedical Relation Extraction Based On Revised Fine-tuning Mechanism IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we will investigate the method of utilizing the entire layer in the fine-tuning process of BERT model. |
Peng Su; K. Vijay-Shanker; | arxiv-cs.CL | 2020-10-31 |
987 | Towards Accurate And Consistent Evaluation: A Dataset For Distantly-Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To solve this problem, we build a new dataset NYTH, where we use the DS-generated data as training data and hire annotators to label test data. |
TONG ZHU et. al. | arxiv-cs.CL | 2020-10-30 |
988 | A Sui Generis QA Approach Using RoBERTa For Adverse Drug Event Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. |
Harshit Jain; Nishant Raj; Suyash Mishra; | arxiv-cs.CL | 2020-10-30 |
989 | Logic-guided Semantic Representation Learning For Zero-Shot Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification. |
JUAN LI et. al. | arxiv-cs.CL | 2020-10-30 |
990 | RuREBus: A Case Study Of Joint Named Entity Recognition And Relation Extraction From E-Government Domain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we describe the whole developed pipeline, starting from text annotation, baseline development, and designing a shared task in hopes of improving the baseline. |
VITALY IVANIN et. al. | arxiv-cs.CL | 2020-10-29 |
991 | RH-Net: Improving Neural Relation Extraction Via Reinforcement Learning and Hierarchical Relational Searching Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel framework named RH-Net, which utilizes Reinforcement learning and Hierarchical relational searching module to improve relation extraction. |
Jianing Wang; | arxiv-cs.CL | 2020-10-27 |
992 | WNUT-2020 Task 1 Overview: Extracting Entities And Relations From Wet Lab Protocols IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents the results of the wet lab information extraction task at WNUT 2020. |
Jeniya Tabassum; Sydney Lee; Wei Xu; Alan Ritter; | arxiv-cs.CL | 2020-10-27 |
993 | Meta-Learning For Neural Relation Classification With Distant Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a meta-learning based approach, which learns to reweight noisy training data under the guidance of reference data. |
ZHENZHEN LI et. al. | arxiv-cs.CL | 2020-10-26 |
994 | TPLinker: Single-stage Joint Extraction Of Entities And Relations Through Token Pair Linking IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias. |
YUCHENG WANG et. al. | arxiv-cs.CL | 2020-10-26 |
995 | Effective Distant Supervision for Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a method of automatically collecting distantly-supervised examples of temporal relations. |
Xinyu Zhao; Shih-ting Lin; Greg Durrett; | arxiv-cs.CL | 2020-10-23 |
996 | Learning To Decouple Relations: Few-Shot Relation Classification With Entity-Guided Attention And Confusion-Aware Training IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate the above relation confusion problem, we propose CTEG, a model equipped with two mechanisms to learn to decouple these easily-confused relations. |
YINGYAO WANG et. al. | arxiv-cs.CL | 2020-10-21 |
997 | Exploit Multiple Reference Graphs For Semi-supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes. |
Wanli Li; Tieyun Qian; | arxiv-cs.CL | 2020-10-21 |
998 | Enhance Prototypical Network with Text Descriptions for Few-shot Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose TD-Proto, which enhances prototypical network with relation and entity descriptions. |
KAIJIA YANG et. al. | cikm | 2020-10-19 |
999 | T-REX: A Topic-Aware Relation Extraction Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the problem, we propose a Topic-aware Relation EXtraction (T-REX) model. |
Woohwan Jung; Kyuseok Shim; | cikm | 2020-10-19 |
1000 | Cross-sentence N-ary Relation Extraction Using Entity Link and Discourse Relation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an efficient method of extracting n-ary relations from multiple sentences which is called Entity-path and Discourse relation-centric Relation Extractor (EDCRE). |
SANGHAK LEE et. al. | cikm | 2020-10-19 |
1001 | Neural Relation Extraction on Wikipedia Tables for Augmenting Knowledge Graphs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We help close this gap with a neural method that uses contextual information surrounding a table in a Wikipedia article to extract relations between entities appearing in the same row of a table or between the entity of said article and entities appearing in the table. |
Erin Macdonald; Denilson Barbosa; | cikm | 2020-10-19 |
1002 | The RELX Dataset And Matching The Multilingual Blanks For Cross-Lingual Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To overcome this issue, we propose two cross-lingual relation classification models: a baseline model based on Multilingual BERT and a new multilingual pretraining setup, which significantly improves the baseline with distant supervision. For evaluation, we introduce a new public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish, called RELX. We also provide the RELX-Distant dataset, which includes hundreds of thousands of sentences with relations from Wikipedia and Wikidata collected by distant supervision for these languages. |
Abdullatif Köksal; Arzucan Özgür; | arxiv-cs.CL | 2020-10-19 |
1003 | Relation Extraction with Self-determined Graph Convolutional Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel model, the Self-determined Graph Convolutional Network (SGCN), which determines a weighted graph using a self-attention mechanism, rather using any linguistic tool. |
Sunil Kumar Sahu; Derek Thomas; Billy Chiu; Neha Sengupta; Mohammady Mahdy; | cikm | 2020-10-19 |
1004 | Cross-Lingual Relation Extraction With Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a cross-lingual RE approach that does not require any human annotation in a target language or any cross-lingual resources. |
Jian Ni; Taesun Moon; Parul Awasthy; Radu Florian; | arxiv-cs.CL | 2020-10-16 |
1005 | TDRE: A Tensor Decomposition Based Approach For Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: According to effective decomposition methods, we propose the Tensor Decomposition based Relation Extraction (TDRE) approach which is able to extract overlapping triplets and avoid detecting unnecessary entity pairs. |
Bin-Bin Zhao; Liang Li; Hui-Dong Zhang; | arxiv-cs.AI | 2020-10-15 |
1006 | Named Entity Recognition and Relation Extraction Using Enhanced Table Filling By Contextualized Representations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, a novel method for extracting named entities and relations from unstructured text based on the table representation is presented. |
Youmi Ma; Tatsuya Hiraoka; Naoaki Okazaki; | arxiv-cs.CL | 2020-10-15 |
1007 | Joint Constrained Learning for Event-Event Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations. |
Haoyu Wang; Muhao Chen; Hongming Zhang; Dan Roth; | arxiv-cs.CL | 2020-10-13 |
1008 | Visual Relation Of Interest Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Visual Relation of Interest Detection (VROID) task, which aims to detect visual relations that are important for conveying the main content of an image, motivated from the intuition that not all correctly detected relations are really "interesting" in semantics and only a fraction of them really make sense for representing the image main content. |
Fan Yu; Haonan Wang; Tongwei Ren; Jinhui Tang; Gangshan Wu; | mm | 2020-10-12 |
1009 | Video Relation Detection Via Multiple Hypothesis Association IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel relation association method called Multiple Hypothesis Association (MHA). |
ZIXUAN SU et. al. | mm | 2020-10-12 |
1010 | HOSE-Net: Higher Order Structure Embedded Network For Scene Graph Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, in this paper, we propose a Higher Order Structure Embedded Network (HOSE-Net) to mitigate this issue. |
Meng Wei; Chun Yuan; Xiaoyu Yue; Kuo Zhong; | mm | 2020-10-12 |
1011 | Relation Classification As Two-way Span-Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a span-prediction based system for RC and evaluate its performance compared to the embedding based system. |
Amir DN Cohen; Shachar Rosenman; Yoav Goldberg; | arxiv-cs.CL | 2020-10-09 |
1012 | Improving Long-Tail Relation Extraction With Collaborating Relation-Augmented Attention IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel neural network, Collaborating Relation-augmented Attention (CoRA), to handle both the wrong labeling and long-tail relations. |
YANG LI et. al. | arxiv-cs.CL | 2020-10-08 |
1013 | Learning From Context Or Names? An Empirical Study On Neural Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the analyses, we propose an entity-masked contrastive pre-training framework for RE to gain a deeper understanding on both textual context and type information while avoiding rote memorization of entities or use of superficial cues in mentions. |
HAO PENG et. al. | arxiv-cs.CL | 2020-10-05 |
1014 | Semi-supervised Relation Extraction Via Incremental Meta Self-Training IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. |
XUMING HU et. al. | arxiv-cs.CL | 2020-10-05 |
1015 | RDSGAN: Rank-based Distant Supervision Relation Extraction With Generative Adversarial Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel generative neural framework named RDSGAN (Rank-based Distant Supervision GAN) which automatically generates valid instances for distant supervision relation extraction. |
Guoqing Luo; Jiaxin Pan; Min Peng; | arxiv-cs.CL | 2020-09-30 |
1016 | Clustering-based Unsupervised Generative Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an Encoder-Decoder architecture to perform self-supervised learning so the encoder can extract relation information. |
Chenhan Yuan; Ryan Rossi; Andrew Katz; Hoda Eldardiry; | arxiv-cs.CL | 2020-09-26 |
1017 | Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we relax this strong assumption by a weaker distant supervision assumption to address the second issue and propose a novel sentence distribution estimator model to address the first problem. |
Chenhan Yuan; Ryan Rossi; Andrew Katz; Hoda Eldardiry; | arxiv-cs.LG | 2020-09-26 |
1018 | DWIE: An Entity-centric Dataset for Multi-task Document-level Information Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents DWIE, the ‘Deutsche Welle corpus for Information Extraction’, a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. |
Klim Zaporojets; Johannes Deleu; Chris Develder; Thomas Demeester; | arxiv-cs.CL | 2020-09-26 |
1019 | A Comparative Study On Structural And Semantic Properties Of Sentence Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate the aforementioned properties by evaluating the extent to which sentences carrying similar senses are embedded in close proximity sub-spaces, and if we can exploit that structure to align sentences to a knowledge graph. We propose a set of experiments using a widely-used large-scale data set for relation extraction and focusing on a set of key sentence embedding methods. |
Alexander Kalinowski; Yuan An; | arxiv-cs.CL | 2020-09-23 |
1020 | AutoRC: Improving BERT Based Relation Classification Models Via Architecture Search IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design a comprehensive search space for BERT based RC models and employ neural architecture search (NAS) method to automatically discover the design choices mentioned above. |
Wei Zhu; Xipeng Qiu; Yuan Ni; Guotong Xie; | arxiv-cs.CL | 2020-09-22 |
1021 | Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction! IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This meta-analysis emphasizes the need for rigor in the report of both the evaluation setting and the datasets statistics and we call for unifying the evaluation setting in end-to-end RE. |
Bruno Taillé; Vincent Guigue; Geoffrey Scoutheeten; Patrick Gallinari; | arxiv-cs.CL | 2020-09-22 |
1022 | Relation Extraction From Biomedical And Clinical Text: Unified Multitask Learning Framework IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. |
Shweta Yadav; Srivatsa Ramesh; Sriparna Saha; Asif Ekbal; | arxiv-cs.CL | 2020-09-20 |
1023 | RECON: Relation Extraction Using Knowledge Graph Context in A Graph Neural Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). |
ANSON BASTOS et. al. | arxiv-cs.CL | 2020-09-18 |
1024 | Finding Influential Instances for Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the contrary, this work proposes a novel model-agnostic instance sampling method for DS by influence function (IF), namely REIF. |
ZIFENG WANG et. al. | arxiv-cs.LG | 2020-09-16 |
1025 | Deep Learning Approaches For Extracting Adverse Events And Indications Of Dietary Supplements From Clinical Text IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The objective of our work is to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DS) in clinical text. |
Yadan Fan; Sicheng Zhou; Yifan Li; Rui Zhang; | arxiv-cs.IR | 2020-09-16 |
1026 | Leveraging Semantic Parsing For Relation Linking Over Knowledge Bases IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To overcome these challenges, we present SLING, a relation linking framework which leverages semantic parsing using Abstract Meaning Representation (AMR) and distant supervision. |
NANDANA MIHINDUKULASOORIYA et. al. | arxiv-cs.CL | 2020-09-16 |
1027 | The Devil Is The Classifier: Investigating Long Tail Relation Classification With Decoupling Analysis IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we conduct an in-depth empirical investigation into the long-tailed problem and found that pre-trained models with instance-balanced sampling already capture the well-learned representations for all classes; moreover, it is possible to achieve better long-tailed classification ability at low cost by only adjusting the classifier. |
HAIYANG YU et. al. | arxiv-cs.LG | 2020-09-15 |
1028 | On Robustness and Bias Analysis of BERT-based Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we analyze a fine-tuned BERT model from different perspectives using relation extraction. |
LUOQIU LI et. al. | arxiv-cs.CL | 2020-09-14 |
1029 | Relation Detection For Indonesian Language Using Deep Neural Network — Support Vector Machine Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we employ neural network to do relation detection between two named entities for Indonesian Language. |
Ramos Janoah Hasudungan; Ayu Purwarianti; | arxiv-cs.CL | 2020-09-11 |
1030 | UPB At SemEval-2020 Task 6: Pretrained Language Models For Definition Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). |
Andrei-Marius Avram; Dumitru-Clementin Cercel; Costin-Gabriel Chiru; | arxiv-cs.CL | 2020-09-11 |
1031 | Semantic Relations and Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared. |
Vivi Nastase; Stan Szpakowicz; | arxiv-cs.CL | 2020-09-11 |
1032 | Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We empirically show that this graph-based approach quite effectively captures the relations between different entity pairs in a dialogue as it outperforms the state-of-the-art approaches by a significant margin on the benchmark dataset DialogRE. |
Hui Chen; Pengfei Hong; Wei Han; Navonil Majumder; Soujanya Poria; | arxiv-cs.CL | 2020-09-10 |
1033 | Meta-Learning with Sparse Experience Replay for Lifelong Language Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel approach to lifelong learning of language tasks based on meta-learning with sparse experience replay that directly optimizes to prevent forgetting. |
Nithin Holla; Pushkar Mishra; Helen Yannakoudakis; Ekaterina Shutova; | arxiv-cs.CL | 2020-09-10 |
1034 | Revisiting LSTM Networks For Semi-Supervised Text Classification Via Mixed Objective Function IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches. |
Devendra Singh Sachan; Manzil Zaheer; Ruslan Salakhutdinov; | arxiv-cs.CL | 2020-09-08 |
1035 | High-throughput Relation Extraction Algorithm Development Associating Knowledge Articles And Electronic Health Records Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Hi-RES, a framework for high-throughput relation extraction algorithm development. |
Yucong Lin; Keming Lu; Yulin Chen; Chuan Hong; Sheng Yu; | arxiv-cs.LG | 2020-09-07 |
1036 | Extracting Semantic Concepts And Relations From Scientific Publications By Using Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications. |
Fatima N. AL-Aswadi; Huah Yong Chan; Keng Hoon Gan; | arxiv-cs.CL | 2020-09-01 |
1037 | SemEval-2020 Task 6: Definition Extraction From Free Text With The DEFT Corpus IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present DeftEval, a SemEval shared task in which participants must extract definitions from free text using a term-definition pair corpus that reflects the complex reality of definitions in natural language. |
Sasha Spala; Nicholas A Miller; Franck Dernoncourt; Carl Dockhorn; | arxiv-cs.CL | 2020-08-31 |
1038 | A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, inspired by the tree-like relation structures in the medical text, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the medical relation triples into two two binary trees and convert the trees into a word-level tags sequence. |
Xukun Luo; Weijie Liu; Meng Ma; Ping Wang; | arxiv-cs.CL | 2020-08-30 |
1039 | Entity And Evidence Guided Relation Extraction For DocRED Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we pro-pose a joint training frameworkE2GRE(Entity and Evidence Guided Relation Extraction)for this task. |
Kevin Huang; Guangtao Wang; Tengyu Ma; Jing Huang; | arxiv-cs.CL | 2020-08-27 |
1040 | Do Syntax Trees Help Pre-trained Transformers Extract Information? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we systematically study the utility of incorporating dependency trees into pre-trained transformers on three representative information extraction tasks: semantic role labeling (SRL), named entity recognition, and relation extraction. |
Devendra Singh Sachan; Yuhao Zhang; Peng Qi; William Hamilton; | arxiv-cs.CL | 2020-08-20 |
1041 | Distantly Supervised Relation Extraction In Federated Settings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a federated denoising framework to suppress label noise in federated settings. |
Dianbo Sui; Yubo Chen; Kang Liu; Jun Zhao; | arxiv-cs.CL | 2020-08-11 |
1042 | Improving Performance Of Relation Extraction Algorithm Via Leveled Adversarial PCNN And Database Expansion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces database expansion using the Minimum Description Length (MDL) algorithm to expand the database for better relation extraction. |
Diyah Puspitaningrum; | arxiv-cs.IR | 2020-07-29 |
1043 | NoPropaganda At SemEval-2020 Task 11: A Borrowed Approach To Sequence Tagging And Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. |
Ilya Dimov; Vladislav Korzun; Ivan Smurov; | arxiv-cs.CL | 2020-07-25 |
1044 | On The Importance Of Word And Sentence Representation Learning In Implicit Discourse Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We argue that a powerful contextualized representation module, a bilateral multi-perspective matching module, and a global information fusion module are all important to implicit discourse analysis. We propose a novel model to combine these modules together. |
Xin Liu; Jiefu Ou; Yangqiu Song; Xin Jiang; | ijcai | 2020-07-21 |
1045 | A Relation-Specific Attention Network For Joint Entity And Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. |
YUE YUAN et. al. | ijcai | 2020-07-21 |
1046 | Attention As Relation: Learning Supervised Multi-head Self-Attention For Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an attention-based joint model, which mainly contains an entity extraction module and a relation detection module, to address the challenges. |
JIE LIU et. al. | ijcai | 2020-07-21 |
1047 | Asking Effective And Diverse Questions: A Machine Reading Comprehension Based Framework For Joint Entity-Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we improve the existing MRC-based entity-relation extraction model through diverse question answering. |
Tianyang Zhao; Zhao Yan; Yunbo Cao; Zhoujun Li; | ijcai | 2020-07-21 |
1048 | Learning Latent Forests For Medical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel model which treats the dependency structure as a latent variable and induces it from the unstructured text in an end-to-end fashion. |
Zhijiang Guo; Guoshun Nan; Wei LU; Shay B. Cohen; | ijcai | 2020-07-21 |
1049 | Modeling Dense Cross-Modal Interactions For Joint Entity-Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a deep Cross-Modal Attention Network (CMAN) for joint entity and relation extraction. |
Shan Zhao; Minghao Hu; Zhiping Cai; Fang Liu; | ijcai | 2020-07-21 |
1050 | Few-shot Relation Extraction Via Bayesian Meta-learning on Task Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel Bayesian meta-learning approach to effectively learn the posterior distributions of the prototype vectors of tasks, where the initial prior of the prototype vectors is parameterized with a graph neural network on the global task graph. |
Meng Qu; Tianyu Gao; Louis-Pascal Xhonneux; Jian Tang; | icml | 2020-07-11 |
1051 | Few-shot Relation Extraction Via Bayesian Meta-learning On Relation Graphs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel Bayesian meta-learning approach to effectively learn the posterior distribution of the prototype vectors of relations, where the initial prior of the prototype vectors is parameterized with a graph neural network on the global relation graph. |
Meng Qu; Tianyu Gao; Louis-Pascal A. C. Xhonneux; Jian Tang; | arxiv-cs.LG | 2020-07-05 |
1052 | Pynsett: A Programmable Relation Extractor Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a programmable relation extraction method for the English language by parsing texts into semantic graphs. |
Alberto Cetoli; | arxiv-cs.CL | 2020-07-04 |
1053 | An Anti-noise Φ-OTDR Based Distributed Acoustic Sensing System for High-speed Railway Intrusion Detection IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We present an anti-noise ϕ-optical time-domain reflectometer-based distributed acoustic sensing system that can effectively differentiate noise and interference for high-speed … |
ZHONGQI LI et. al. | Laser Physics | 2020-06-30 |
1054 | NLPContributions: An Annotation Scheme For Machine Reading Of Scholarly Contributions In Natural Language Processing Literature IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this article, we describe the outcomes of this pilot annotation phase. |
Jennifer D’Souza; Sören Auer; | arxiv-cs.CL | 2020-06-23 |
1055 | SpanMlt: A Span-based Multi-Task Learning Framework For Pair-wise Aspect And Opinion Terms Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE). |
He Zhao; Longtao Huang; Rong Zhang; Quan Lu; hui xue; | acl | 2020-06-20 |
1056 | Reasoning With Latent Structure Refinement For Document-Level Relation Extraction IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. |
Guoshun Nan; Zhijiang Guo; Ivan Sekulic; Wei Lu; | acl | 2020-06-20 |
1057 | TACRED Revisited: A Thorough Evaluation Of The TACRED Relation Extraction Task IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for improvement? |
Christoph Alt; Aleksandra Gabryszak; Leonhard Hennig; | acl | 2020-06-20 |
1058 | In Layman’s Terms: Semi-Open Relation Extraction From Scientific Texts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we combine the output of both types of systems to achieve Semi-Open Relation Extraction, a new task that we explore in the Biology domain. |
Ruben Kruiper; Julian Vincent; Jessica Chen-Burger; Marc Desmulliez; Ioannis Konstas; | acl | 2020-06-20 |
1059 | Exploiting The Syntax-Model Consistency For Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization. |
Amir Pouran Ben Veyseh; Franck Dernoncourt; Dejing Dou; Thien Huu Nguyen; | acl | 2020-06-20 |
1060 | Generalizing Natural Language Analysis Through Span-relation Representations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks. |
Zhengbao Jiang; Wei Xu; Jun Araki; Graham Neubig; | acl | 2020-06-20 |
1061 | Synchronous Double-channel Recurrent Network For Aspect-Opinion Pair Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs. To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks. |
Shaowei Chen; Jie Liu; Yu Wang; Wenzheng Zhang; Ziming Chi; | acl | 2020-06-20 |
1062 | ExpBERT: Representation Engineering With Natural Language Explanations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we allow model developers to specify these types of inductive biases as natural language explanations. |
Shikhar Murty; Pang Wei Koh; Percy Liang; | acl | 2020-06-20 |
1063 | Relabel The Noise: Joint Extraction Of Entities And Relations Via Cooperative Multiagents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a joint extraction approach to address this problem by re-labeling noisy instances with a group of cooperative multiagents. |
Daoyuan Chen; Yaliang Li; Kai Lei; Ying Shen; | acl | 2020-06-20 |
1064 | ZeroShotCeres: Zero-Shot Relation Extraction From Semi-Structured Webpages IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a solution for zero-shot open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. |
Colin Lockard; Prashant Shiralkar; Xin Luna Dong; Hannaneh Hajishirzi; | acl | 2020-06-20 |
1065 | Dialogue-Based Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. |
Dian Yu; Kai Sun; Claire Cardie; Dong Yu; | acl | 2020-06-20 |
1066 | Probing Linguistic Features Of Sentence-Level Representations In Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce 14 probing tasks targeting linguistic properties relevant to RE, and we use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets, TACRED and SemEval 2010 Task 8. |
Christoph Alt; Aleksandra Gabryszak; Leonhard Hennig; | acl | 2020-06-20 |
1067 | Implicit Discourse Relation Classification: We Need To Talk About Evaluation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we highlight these inconsistencies and propose an improved evaluation protocol. |
Najoung Kim; Song Feng; Chulaka Gunasekara; Luis Lastras; | acl | 2020-06-20 |
1068 | Revisiting Unsupervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we demonstrate that by using only named entities to induce relation types, we can outperform existing methods on two popular datasets. |
Thy Thy Tran; Phong Le; Sophia Ananiadou; | acl | 2020-06-20 |
1069 | Relation Extraction With Explanation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we annotate a test set with ground-truth sentence-level explanations to evaluate the quality of explanations afforded by the relation extraction models. |
Hamed Shahbazi; Xiaoli Fern; Reza Ghaeini; Prasad Tadepalli; | acl | 2020-06-20 |
1070 | Extracting N-ary Cross-sentence Relations Using Constrained Subsequence Kernel Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new formulation of the relation extraction task where the relations are more general than intra-sentence relations in the sense that they may span multiple sentences and may have more than two arguments. |
Sachin Pawar; Pushpak Bhattacharyya; Girish K. Palshikar; | arxiv-cs.CL | 2020-06-15 |
1071 | Cascaded Human-Object Interaction Recognition IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Considering the intrinsic complexity of the task, we introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. |
Tianfei Zhou; Wenguan Wang; Siyuan Qi; Haibin Ling; Jianbing Shen; | cvpr | 2020-06-08 |
1072 | Beyond Short-Term Snippet: Video Relation Detection With Spatio-Temporal Global Context IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address these issues, this work proposes a novel sliding-window scheme to simultaneously predict short-term and long-term relationships. |
Chenchen Liu; Yang Jin; Kehan Xu; Guoqiang Gong; Yadong Mu; | cvpr | 2020-06-08 |
1073 | Semantic Loss Application To Entity Relation Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main contribution of this paper is an end-to-end neural model for joint entity relation extraction which incorporates a novel loss function. |
Venkata Sasank Pagolu; | arxiv-cs.CL | 2020-06-06 |
1074 | Relation Of The Relations: A New Paradigm Of The Relation Extraction Problem IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Due to the significance of RoR in existing datasets, we propose a new paradigm of RE that considers as a whole the predictions of all relations in the same context. |
Zhijing Jin; Yongyi Yang; Xipeng Qiu; Zheng Zhang; | arxiv-cs.CL | 2020-06-05 |
1075 | Benchmarking BioRelEx For Entity Tagging And Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to fill this gap, we compare multiple existing entity and relation extraction models over a recently introduced public dataset, BioRelEx of sentences annotated with biological entities and relations. |
Abhinav Bhatt; Kaustubh D. Dhole; | arxiv-cs.CL | 2020-05-31 |
1076 | Relation Extraction With Explanation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we annotate a test set with ground-truth sentence-level explanations to evaluate the quality of explanations afforded by the relation extraction models. |
Hamed Shahbazi; Xiaoli Z. Fern; Reza Ghaeini; Prasad Tadepalli; | arxiv-cs.IR | 2020-05-28 |
1077 | A Data-driven Approach For Noise Reduction In Distantly Supervised Biomedical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction. |
Saadullah Amin; Katherine Ann Dunfield; Anna Vechkaeva; Günter Neumann; | arxiv-cs.CL | 2020-05-26 |
1078 | Semi-Automating Knowledge Base Construction For Cancer Genetics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we consider the exponentially growing subarea of genetics in cancer. |
Somin Wadhwa; Kanhua Yin; Kevin S. Hughes; Byron C. Wallace; | arxiv-cs.CL | 2020-05-16 |
1079 | A Scientific Information Extraction Dataset For Nature Inspired Engineering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. |
Ruben Kruiper; Julian F. V. Vincent; Jessica Chen-Burger; Marc P. Y. Desmulliez; Ioannis Konstas; | arxiv-cs.CL | 2020-05-15 |
1080 | In Layman’s Terms: Semi-Open Relation Extraction From Scientific Texts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we combine the output of both types of systems to achieve Semi-Open Relation Extraction, a new task that we explore in the Biology domain. |
Ruben Kruiper; Julian F. V. Vincent; Jessica Chen-Burger; Marc P. Y. Desmulliez; Ioannis Konstas; | arxiv-cs.CL | 2020-05-15 |
1081 | In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Information Extraction (IE) from scientific texts can be used to guide readers to the central information in scientific documents. But narrow IE systems extract only a fraction of … |
Ruben Kruiper; J. Vincent; Jessica Chen-Burger; M. Desmulliez; Ioannis Konstas; | ArXiv | 2020-05-15 |
1082 | LOREM: Language-consistent Open Relation Extraction From Unstructured Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a Language-consistent multi-lingual Open Relation Extraction Model (LOREM) for finding relation tuples of any type between entities in unstructured texts. |
Tom Harting; Sepideh Mesbah; Christoph Lofi; | www | 2020-05-13 |
1083 | NERO: A Neural Rule Grounding Framework For Label-Efficient Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a neural approach to ground rules for RE, named Nero, which jointly learns a relation extraction module and a soft matching module. |
WENXUAN ZHOU et. al. | www | 2020-05-13 |
1084 | Reasoning With Latent Structure Refinement For Document-Level Relation Extraction IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. |
Guoshun Nan; Zhijiang Guo; Ivan Sekulić; Wei Lu; | arxiv-cs.CL | 2020-05-13 |
1085 | PERLEX: A Bilingual Persian-English Gold Dataset For Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present PERLEX as the first Persian dataset for relation extraction, which is an expert-translated version of the Semeval-2010-Task-8 dataset. |
Majid Asgari-Bidhendi; Mehrdad Nasser; Behrooz Janfada; Behrouz Minaei-Bidgoli; | arxiv-cs.CL | 2020-05-13 |
1086 | Relation Adversarial Network For Low Resource Knowledge Graph Completion IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available. |
NINGYU ZHANG et. al. | www | 2020-05-13 |
1087 | Adversarial Learning For Supervised And Semi-supervised Relation Extraction In Biomedical Literature Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate adversarial training with multiple adversarial examples to benefit the relation extraction task. |
Peng Su; K. Vijay-Shanker; | arxiv-cs.CL | 2020-05-08 |
1088 | Exploring The Suitability Of Semantic Spaces As Word Association Models For The Extraction Of Semantic Relationships Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we empirically study and explore the potential of a novel idea of using classical semantic spaces and models, e.g., Word Embedding, generated for extracting word association, in conjunction with relation extraction approaches. |
Epaminondas Kapetanios; Vijayan Sugumaran; Anastassia Angelopoulou; | arxiv-cs.CL | 2020-04-29 |
1089 | Distantly-Supervised Neural Relation Extraction With Side Information Using BERT Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Considering that this method outperformed state-of-the-art baselines, in this paper, we propose a related approach to RESIDE also using additional side information, but simplifying the sentence encoding with BERT embeddings. |
Johny Moreira; Chaina Oliveira; David Macêdo; Cleber Zanchettin; Luciano Barbosa; | arxiv-cs.CL | 2020-04-29 |
1090 | A Practical Framework For Relation Extraction With Noisy Labels Based On Doubly Transitional Loss Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this issue, we introduce a practical end-to-end deep learning framework, including a standard feature extractor and a novel noisy classifier with our proposed doubly transitional mechanism. |
Shanchan Wu; Kai Fan; | arxiv-cs.CL | 2020-04-28 |
1091 | MICK: A Meta-Learning Framework For Few-shot Relation Classification With Small Training Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we tackle an even harder problem by further limiting the amount of data available at training time. Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a few-shot relation classification dataset in health domain with purposely small training data and challenging relation classes. |
Xiaoqing Geng; Xiwen Chen; Kenny Q. Zhu; Libin Shen; Yinggong Zhao; | arxiv-cs.CL | 2020-04-26 |
1092 | Contextualised Graph Attention For Improved Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a contextualized graph attention network that combines edge features and multiple sub-graphs for improving relation extraction. |
Angrosh Mandya; Danushka Bollegala; Frans Coenen; | arxiv-cs.CL | 2020-04-22 |
1093 | Learning Relation Ties With A Force-Directed Graph In Distant Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve this problem, in this paper, we propose a novel force-directed graph based relation extraction model to comprehensively learn relation ties. |
Yuming Shang; Heyan Huang; Xin Sun; Xianling Mao; | arxiv-cs.CL | 2020-04-21 |
1094 | Improving Neural Relation Extraction With Implicit Mutual Relations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible. |
J. Kuang; Y. Cao; J. Zheng; X. He; M. Gao and A. Zhou; | icde | 2020-04-20 |
1095 | Probing Linguistic Features Of Sentence-Level Representations In Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce 14 probing tasks targeting linguistic properties relevant to RE, and we use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets, TACRED and SemEval 2010 Task 8. |
Christoph Alt; Aleksandra Gabryszak; Leonhard Hennig; | arxiv-cs.CL | 2020-04-17 |
1096 | Improving Scholarly Knowledge Representation: Evaluating BERT-based Models For Scientific Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present a thorough empirical evaluation on eight Bert-based classification models by focusing on two key factors: 1) Bert model variants, and 2) classification strategies. |
Ming Jiang; Jennifer D’Souza; Sören Auer; J. Stephen Downie; | arxiv-cs.DL | 2020-04-13 |
1097 | Relation Transformer Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel transformer formulation for scene graph generation and relation prediction. |
Rajat Koner; Suprosanna Shit; Volker Tresp; | arxiv-cs.CV | 2020-04-13 |
1098 | Robustly Pre-trained Neural Model For Direct Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Methods: We studied several variants of BERT (Bidirectional Encoder Representations using Transformers) some involving clinical domain customization and the others involving improved architecture and/or training strategies. |
Hong Guan; Jianfu Li; Hua Xu; Murthy Devarakonda; | arxiv-cs.CL | 2020-04-13 |
1099 | Efficient Long-distance Relation Extraction With DG-SpanBERT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBERT) that extracts semantic features from a raw sentence using the pre-trained language model SpanBERT and a graph convolutional network to pool latent features. |
Jun Chen; Robert Hoehndorf; Mohamed Elhoseiny; Xiangliang Zhang; | arxiv-cs.CL | 2020-04-07 |
1100 | Downstream Model Design Of Pre-trained Language Model For Relation Extraction Task IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, a new network architecture with a special loss function is designed to serve as a downstream model of PLMs for supervised relation extraction. |
Cheng Li; Ye Tian; | arxiv-cs.CL | 2020-04-07 |
1101 | A Corpus Study And Annotation Schema For Named Entity Recognition And Relation Extraction Of Business Products Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a corpus study, an annotation schema and associated guidelines, for the annotation of product entity and company-product relation mentions. We also describe our ongoing annotation effort, and present a preliminary corpus of English web and social media documents annotated according to the proposed guidelines. |
Saskia Schön; Veselina Mironova; Aleksandra Gabryszak; Leonhard Hennig; | arxiv-cs.CL | 2020-04-07 |
1102 | A German Corpus For Fine-Grained Named Entity Recognition And Relation Extraction Of Traffic And Industry Events IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work describes a corpus of German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. |
MARTIN SCHIERSCH et. al. | arxiv-cs.CL | 2020-04-07 |
1103 | More Data, More Relations, More Context And More Openness: A Review And Outlook For Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. |
XU HAN et. al. | arxiv-cs.CL | 2020-04-07 |
1104 | SelfORE: Self-supervised Relational Feature Learning For Open Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we proposed a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. |
Xuming Hu; Chenwei Zhang; Yusong Xu; Lijie Wen; Philip S. Yu; | arxiv-cs.CL | 2020-04-06 |
1105 | Named Entities In Medical Case Reports: Corpus And Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the case reports, we annotate cases, conditions, findings, factors and negation modifiers. We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central’s open access library. |
Sarah Schulz; Jurica Ševa; Samuel Rodriguez; Malte Ostendorff; Georg Rehm; | arxiv-cs.CL | 2020-03-29 |
1106 | Common-Knowledge Concept Recognition For SEVA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We use a pre-trained language model and fine-tune it with the labeled dataset of concepts. With the help of a domain expert and text processing methods, we construct a dataset annotated at the word-level by carefully defining a labelling scheme to train a sequence model to recognize systems engineering concepts. In addition, we also create some essential datasets for information such as abbreviations and definitions from the systems engineering domain. |
Jitin Krishnan; Patrick Coronado; Hemant Purohit; Huzefa Rangwala; | arxiv-cs.CL | 2020-03-25 |
1107 | Hybrid Attention-Based Transformer Block Model For Distant Supervision Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this issue, we propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task. |
Yan Xiao; Yaochu Jin; Ran Cheng; Kuangrong Hao; | arxiv-cs.CL | 2020-03-10 |
1108 | Unsupervised Adversarial Domain Adaptation For Implicit Discourse Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an unsupervised adversarial domain adaptive network equipped with a reconstruction component. |
Hsin-Ping Huang; Junyi Jessy Li; | arxiv-cs.CL | 2020-03-04 |
1109 | Unique Chinese Linguistic Phenomena Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Linguistics holds unique characteristics of generality, stability, and nationality, which will affect the formulation of extraction strategies and should be incorporated into the … |
Shengbin Jia; | arxiv-cs.CL | 2020-02-23 |
1110 | Neural Relation Prediction For Simple Question Answering Over Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an instance-based method to capture the underlying relation of question and to this aim, we detect matching paraphrases of a new question which share the same relation, and their corresponding relation is selected as our prediction. |
Amin Abolghasemi; Saeedeh Momtazi; | arxiv-cs.CL | 2020-02-18 |
1111 | Deeper Task-Specificity Improves Joint Entity And Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, we introduce additional task-specific bidirectional RNN layers for both the NER and RE tasks and tune the number of shared and task-specific layers separately for different datasets. |
Phil Crone; | arxiv-cs.CL | 2020-02-15 |
1112 | Localize, Assemble, and Predicate: Contextual Object Proposal Embedding for Visual Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this problem, we propose localize-assemble-predicate network (LAP-Net), which decomposes VRD into three sub-tasks: localizing individual objects, assembling and predicting the subject-object pairs. |
Ruihai Wu; Kehan Xu; Chenchen Liu; Nan Zhuang; Yadong Mu; | aaai | 2020-02-07 |
1113 | Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose two approaches to use encoder-decoder architecture for jointly extracting entities and relations. |
Tapas Nayak; Hwee Tou Ng; | aaai | 2020-02-07 |
1114 | One-Shot Learning for Long-Tail Visual Relation Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With this in mind, we designed a novel model for visual relation detection that works in one-shot settings. |
WEITAO WANG et. al. | aaai | 2020-02-07 |
1115 | Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-Level Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. |
Trapit Bansal; Pat Verga; Neha Choudhary; Andrew McCallum; | aaai | 2020-02-07 |
1116 | Distilling Knowledge from Well-Informed Soft Labels for Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to explore the supervision with soft labels in relation extraction, which makes it possible to integrate prior knowledge. |
ZHENYU ZHANG et. al. | aaai | 2020-02-07 |
1117 | Neural Snowball for Few-Shot Relation Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. |
TIANYU GAO et. al. | aaai | 2020-02-07 |
1118 | Integrating Relation Constraints with Neural Relation Extractors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, Constraint Loss. |
Yuan Ye; Yansong Feng; Bingfeng Luo; Yuxuan Lai; Dongyan Zhao; | aaai | 2020-02-07 |
1119 | Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework. |
YANG LI et. al. | aaai | 2020-02-07 |
1120 | Multi-View Consistency for Relation Extraction Via Mutual Information and Structure Prediction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. |
Amir Veyseh; Franck Dernoncourt; My Thai; Dejing Dou; Thien Nguyen; | aaai | 2020-02-07 |
1121 | Are Noisy Sentences Useless for Distant Supervised Relation Extraction? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences. |
Yuming Shang; He-Yan Huang; Xian-Ling Mao; Xin Sun; Wei Wei; | aaai | 2020-02-07 |
1122 | Joint Entity and Relation Extraction with A Hybrid Transformer and Reinforcement Learning Based Model IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a hybrid deep neural network model to jointly extract the entities and relations, and the model is also capable of filtering noisy data. |
Ya Xiao; Chengxiang Tan; Zhijie Fan; Qian Xu; Wenye Zhu; | aaai | 2020-02-07 |
1123 | Integrating Deep Learning with Logic Fusion for Information Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner. |
Wenya Wang; Sinno Jialin Pan; | aaai | 2020-02-07 |
1124 | Improving Neural Relation Extraction with Positive and Unlabeled Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. |
ZHENGQIU HE et. al. | aaai | 2020-02-07 |
1125 | Relation Extraction with Convolutional Network Over Learnable Syntax-Transport Graph IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we learn to transform the dependency tree into a weighted graph by considering the syntax dependencies of the connected nodes and persisting the structure of the original dependency tree. |
Kai Sun; Richong Zhang; Yongyi Mao; Samuel Mensah; Xudong Liu; | aaai | 2020-02-07 |
1126 | Relation Extraction Exploiting Full Dependency Forests IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose to leverage full dependency forests for this task, where a full dependency forest encodes all possible trees. |
LIFENG JIN et. al. | aaai | 2020-02-07 |
1127 | A Neural Architecture For Person Ontology Population Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a system for automatically populating a person ontology graph from unstructured data using neural models for Entity Classification and Relation Extraction. We introduce a new dataset for these tasks and discuss our results. |
Balaji Ganesan; Riddhiman Dasgupta; Akshay Parekh; Hima Patel; Berthold Reinwald; | arxiv-cs.AI | 2020-01-22 |
1128 | BiOnt: Deep Learning Using Multiple Biomedical Ontologies For Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current … |
Diana Sousa; Francisco M. Couto; | arxiv-cs.IR | 2020-01-20 |
1129 | A Logic-based Relational Learning Approach To Relation Extraction: The OntoILPER System IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present OntoILPER, a logic-based relational learning approach to Relation Extraction that uses Inductive Logic Programming for generating extraction models in the form of symbolic extraction rules. |
Rinaldo Lima; Bernard Espinasse; Fred Freitas; | arxiv-cs.AI | 2020-01-13 |
1130 | A Neural Approach To Discourse Relation Signal Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we present a data-driven approach to signal detection using a distantly supervised neural network and develop a metric, Delta s (or ‘delta-softmax’), to quantify signaling strength. |
Amir Zeldes; Yang Liu; | arxiv-cs.CL | 2020-01-08 |
1131 | Amharic Open Information Extraction with Syntactic Sentence Simplification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Open Information Extraction (OIE) is the process of discovering domain-independent relations from natural language text. It has recently received increased attention and been … |
Seble Girma; Yaregal Assabie; | 2020-01-01 | |
1132 | Medical Entity Extraction from Health Insurance Documents Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The task of named entity recognition is to identify certain types of entities with special meanings from the text. It is a basic task in natural language processing and the … |
Tianling Pu; Qifan Zhang; Junjie Yao; Yingjie Zhang; | 2020 IEEE International Conference on Knowledge Graph (ICKG) | 2020-01-01 |
1133 | Information Extraction from Text Intensive and Visually Rich Banking Documents IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document types, where visual and textual information plays an important role in their analysis and understanding, pose a new and attractive area for information extraction … |
Berke Oral; Erdem Emekligil; Secil Arslan; Gülsen Eryiǧit; | Inf. Process. Manag. | 2020-01-01 |
1134 | A Gated Piecewise CNN with Entity-aware Enhancement for Distantly Supervised Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The piecewise convolutional neural network (PCNN) is an important method for distant supervision relation extraction. However, the existing methods based on the PCNN still have … |
Haixu Wen; Xinhua Zhu; Lanfang Zhang; Fei Li; | Inf. Process. Manag. | 2020-01-01 |
1135 | Extraction of A Knowledge Graph from French Cultural Heritage Documents IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Cultural heritage in Quebec is often represented as collections of French documents that contain a lot of valuable, yet unstructured, data. One of the current aims of the Quebec … |
Erwan Marchand; Michel Gagnon; Amal Zouaq; | 2020-01-01 | |
1136 | Chinese Relation Extraction Based on Lattice Network Improved with BERT Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is a basic and important task in the field of natural language processing(NLP). There are already many researches on English dataset, but the researches on … |
Zhengchun Zhang; Qingsong Yu; | Proceedings of the 2020 5th International Conference on … | 2020-01-01 |
1137 | Few-shot Relation Classification By Context Attention-based Prototypical Networks with BERT IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Human-computer interaction under the cloud computing platform is very important, but the semantic gap will limit the performance of interaction. It is necessary to understand the … |
Bei Hui; Liang Liu; Jia Chen; Xue Zhou; Yuhui Nian; | EURASIP Journal on Wireless Communications and Networking | 2020-01-01 |
1138 | A Latent-label Denoising Method for Relation Extraction with Self-directed Confidence Learning Related Papers Related Patents Related Grants Related Venues Related Experts View |
Tingting Sun; Chunhong Zhang; Yang Ji; Zheng Hu; | Intell. Data Anal. | 2020-01-01 |
1139 | End-to-end Relation Extraction Based on Bootstrapped Multi-level Distant Supervision Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervised relation extraction has been widely used to identify new relation facts from free text, since the existence of knowledge base helps these models to build a … |
YING HE et. al. | World Wide Web | 2020-01-01 |
1140 | A Thesaurus Based Semantic Relation Extraction for Agricultural Corpora Related Papers Related Patents Related Grants Related Venues Related Experts View |
R. Srinivasan; C. N. Subalalitha; | 2020-01-01 | |
1141 | Extracting Events and Their Relations from Texts: A Survey on Recent Research Progress and Challenges IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Event is a common but non-negligible knowledge type. How to identify events from texts, extract their arguments, even analyze the relations between different events are important … |
Kang Liu; Yubo Chen; Jian Liu; Xinyu Zuo; Jun Zhao; | AI Open | 2020-01-01 |
1142 | Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification (Student Abstract) Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong … |
Yuxiang Xie; Hua Xu; Congcong Yang; Kai Gao; | 2020-01-01 | |
1143 | Relation Extraction for Competitive Intelligence Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Competitive intelligence (CI) has become one of the major subjects for strategic process in an organization in the recent years. CI gives support to the strategic business area … |
Sandra Collovini; Patrícia Nunes Gonçalves; Guilherme Cavalheiro; Joaquim Santos; Renata Vieira; | 2020-01-01 | |
1144 | Surface Pattern-enhanced Relation Extraction with Global Constraints Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is one of the most important tasks in information extraction. The traditional works either use sentences or surface patterns (i.e., the shortest dependency … |
HAIYUN JIANG et. al. | Knowledge and Information Systems | 2020-01-01 |
1145 | Joint Model of Entity Recognition and Relation Extraction Based on Artificial Neural Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity and relationship extraction is an important step in building a knowledge base, which is the basis for many artificial intelligence products to be used in life, such as … |
Zhu Zhang; Shu Zhan; Haiyan Zhang; Xinke Li; | Journal of Ambient Intelligence and Humanized Computing | 2020-01-01 |
1146 | Deep Neural Architectures for End-to-End Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Tung Tran; | 2020-01-01 | |
1147 | Effective Piecewise CNN with Attention Mechanism for Distant Supervision on Relation Extraction Task Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yuming Li; Pin Ni; Gangmin Li; Victor I. Chang; | 2020-01-01 | |
1148 | Encrypted Traffic Classification Based on Gaussian Mixture Models and Hidden Markov Models IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: To protect user privacy (e.g., IP address and sensitive data in a packet), many traffic protection methods, like traffic obfuscation and encryption technologies, are introduced. … |
ZHONGJIANG YAO et. al. | J. Netw. Comput. Appl. | 2020-01-01 |
1149 | Relation Classification: How Well Do Neural Network Approaches Work? Related Papers Related Patents Related Grants Related Venues Related Experts View |
Sri Nath Dwivedi; Harish Karnick; Renu Jain; | 2020-01-01 | |
1150 | Knowledge-embodied Attention for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Kejun Deng; Xuemiao Zhang; Songtao Ye; Junfei Liu; | Intell. Data Anal. | 2020-01-01 |
1151 | Axiomatic Relation Extraction from Text in The Domain of Tourism Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ana B. Ríos-Alvarado; José-Lázaro Martínez-Rodríguez; Tania Yukary Guerrero-Melendez; Adolfo J. Rodriguez-Rodriguez; David Tomás Vargas-Requena; | 2020-01-01 | |
1152 | Named Entity Disambiguation at Scale Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Named Entity Disambiguation (NED) is a crucial task in many Natural Language Processing applications such as entity linking, record linkage, knowledge base construction, or … |
Ahmad Aghaebrahimian; Mark Cieliebak; | 2020-01-01 | |
1153 | Explaining Spatial Relation Detection Using Layerwise Relevance Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View |
Gabriel Farrugia; Adrian Muscat; | 2020-01-01 | |
1154 | Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on A Double Attention Mechanism Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relationship extraction among diseases, symptoms and tests has always been a concerning research issue in the biomedical field. Disease-pertinent relationship extraction for … |
Yanli Zhang; Xinmiao Li; Zhe Zhang; | IEEE Access | 2020-01-01 |
1155 | Heterogeneous Graph Neural Networks for Noisy Few-shot Relation Classification IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is an essential and fundamental task in natural language processing. Distant supervised methods have achieved great success on relation classification, … |
Yuxiang Xie; Hua Xu; Jiaoe Li; Congcong Yang; Kai Gao; | Knowl. Based Syst. | 2020-01-01 |
1156 | Proposal for Named Entities Recognition and Classification (NERC) and The Automatic Generation of Rules on Mexican News Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper we introduce a proposal for extracting facts from news on Mexican online newspapers through their RSS (Really Simple Syndication). This problem will be addressed by … |
Orlando Ramos Flores; David Pinto; | Computación y Sistemas | 2020-01-01 |
1157 | Extracting Deep Personae Social Relations in Microblog Posts Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Numerous studies have been conducted to extract relationships from different documents. However, extracting relationships from microblog posts is rarely studied. In this paper, we … |
Yajun Du; Fanghong Su; Anzheng Yang; Xianyong Li; Yongquan Fan; | IEEE Access | 2020-01-01 |
1158 | Survey of Current State of The Art Entity-Relation Extraction Tools Related Papers Related Patents Related Grants Related Venues Related Experts View |
Katrina Ward; Jonathan Bisila; Kelsey Cairns; | 2020-01-01 | |
1159 | Bidirectional Gated Recurrent Unit Neural Networks for Relation Extraction of Chinese Enterprises Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: With the improvement of people’s entrepreneurial awareness and the encouragement of policies, the number of enterprises is gradually increasing. However, the profit-seeking nature … |
Nian Yang; Dongxin Shi; Yan Hua; | 2020 IEEE 4th Information Technology, Networking, … | 2020-01-01 |
1160 | RENERSANs: RELATION EXTRACTION AND NAMED ENTITY RECOGNITION AS SEQUENCE ANNOTATION Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this work we present our system for RuREBus shared task held together with Dialog 2020 conference. The task consisted of 3 subtasks: named entity recognition, relation … |
A. A. Davletov; D. I. Gordeev; A. I. Rey; N. V. Arefyev; | 2020-01-01 | |
1161 | Extracting Chinese Domain-specific Open Entity and Relation By Using Learning Patterns Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Nowadays, Chinese domain-specific relation extraction faces a major challenge, that is the lack of annotation data. To cope with this challenge, the distant supervision which can … |
Hongying Wen; Zhiguang Wang; Qiang Lu; | Proceedings of the ACM Turing Celebration Conference – China | 2020-01-01 |
1162 | An Extensible Framework of Leveraging Syntactic Skeleton for Semantic Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is one of the most fundamental upstream tasks in natural language processing and information extraction. State-of-the-art approaches make use of various … |
Hao Wang; Qiongxing Tao; Siyuan Du; Xiangfeng Luo; | ACM Transactions on Asian and Low-Resource Language … | 2020-01-01 |
1163 | An Integration Model Based on Graph Convolutional Network for Text Classification IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Graph Convolutional Network (GCN) is extensively used in text classification tasks and performs well in the process of the non-euclidean structure data. Usually, GCN is … |
Hengliang Tang; Yuan Mi; Fei Xue; Yang Cao; | IEEE Access | 2020-01-01 |
1164 | A Method of Relation Extraction Using Pre-training Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Extraction (RE), as an essential task of Natural Language Processing (NLP), aims to extract potential relations between two entities in a sentence. It is a crucial step … |
YU WANG et. al. | 2020 13th International Symposium on Computational … | 2020-01-01 |
1165 | Learning Labeling Functions in Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yaocheng Gui; Qian Liu; Zhiqiang Gao; | Intell. Data Anal. | 2020-01-01 |
1166 | A Robust Graph Convolutional Network for Relation Extraction By Combining Edge Information Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Graph convolutional network was widely used in natural language processing this year and achieved great success. However, existed Graph-based methods do not account for the edge … |
Haiqiang Chen; Luping Liu; Xin Zhou; Linbo Qing; Meiling Wang; | 2020 IEEE 5th International Conference on Cloud Computing … | 2020-01-01 |
1167 | Sentence Relation Classification Using Deep Learning Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View |
B. Haripriya; Kishorjit Nongmeikapam; | 2020-01-01 | |
1168 | A Brief Review of Relation Extraction Based on Pre-Trained Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is to extract the semantic relation between entity pairs in text, and it is a key point in building Knowledge Graphs and information extraction. The rapid … |
Tiange Xu; Fu Zhang; | 2020-01-01 | |
1169 | Creating A Large-Scale Financial News Corpus for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As the data accessible is growing exponentially, the unstructured information has become the most common and main resource for decision makers. News, one of the most basic … |
Haoyu Wu; Qing Lei; Xinyue Zhang; Zhengqian Luo; | 2020 3rd International Conference on Artificial … | 2020-01-01 |
1170 | Research on Relation Extraction Method of Chinese Electronic Medical Records Based on BERT Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a necessary step in obtaining information from electronic medical records. The deep learning methods for relation extraction are primarily based on word2vec … |
Shengxin Gao; Jinlian Du; Xiao Zhang; | Proceedings of the 2020 6th International Conference on … | 2020-01-01 |
1171 | Causal Relation Extraction Based on Graph Attention Networks Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xu Jinghang; Zuo Wanli; Liang Shining; Wang Ying; | Journal of Computer Research and Development | 2020-01-01 |
1172 | Learning Relation Entailment with Structured and Textual Information Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relations among words and entities are important for semantic understanding of text, but previous work has largely not considered relations between relations, or meta-relations. … |
ZHENGBAO JIANG et. al. | 2020-01-01 | |
1173 | A Novel Document-Level Relation Extraction Method Based on BERT and Entity Information IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction aims to extract the relationship among the entities in a paragraph of text. Compared with sentence-level, the text in document-level relation … |
Xiaoyu Han; Lei Wang; | IEEE Access | 2020-01-01 |
1174 | Fine-grained Relation Extraction with Focal Multi-task Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Dear editor, Relation extraction aims to identify relation facts for pairs of entities in raw texts to construct triplets such as [Arthur Lee, place born, Memphis]. To … |
Xinsong Zhang; Tianyi Liu; Weijia Jia; Pengshuai Li; | Science China Information Sciences | 2020-01-01 |
1175 | Mining Scientific and Technical Literature Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this chapter, the authors study text mining technologies such as knowledge extraction and summarization on scientific and technical literature. First, they analyze the needs of … |
Junsheng Zhang; Wen Zeng; | 2020-01-01 | |
1176 | Semantic Enhanced Distantly Supervised Relation Extraction Via Graph Attention Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distantly Supervised relation extraction methods can automatically extract the relation between entity pairs, which are essential for the construction of a knowledge graph. … |
Xiaoye Ouyang; Shudong Chen; Rong Wang; | Inf. | 2020-01-01 |
1177 | Joint Extraction of Entity and Relation Based on Pre-trained Language Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting entities and relations from unstructured text is the key to building a large-scale knowledge graph. In recent years, the relation extraction approaches based on the … |
Mingda Zhu; Jiqing Xue; Gaoyuan Zhou; | 2020 12th International Conference on Intelligent … | 2020-01-01 |
1178 | A Spatial Information Inference Method for Programming By Demonstration of Assembly Tasks By Integrating Visual Observation with CAD Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In robot programming by demonstration (PbD) of small parts assembly tasks, the accuracy of parts poses estimated by vision-based techniques in demonstration stage is far from … |
Zhongxiang Zhou; Liang Ji; Rong Xiong; Yue Wang; | Assembly Automation | 2020-01-01 |
1179 | End-to-end Relation Extraction Using Graph Convolutional Network with A Novel Entity Attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: There are more and more researches on joint relation extraction, however, the current popular joint extraction method has more or less limitations, either the training time is too … |
Qi Wang; Li Lv; Bihui Yu; Siqi Li; | 2020 IEEE 6th International Conference on Computer and … | 2020-01-01 |
1180 | RePersian:An Efficient Open Information Extraction Tool in Persian Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is the task of extracting semantic information from raw data. One of the key points in the area of open information extraction systems is the ability to … |
Raana Saheb-Nassagh; Majid Asgari; Behrouz Minaei-Bidgoli; | 2020 6th International Conference on Web Research (ICWR) | 2020-01-01 |
1181 | CFSRE: Context-aware Based on Frame-semantics for Distantly Supervised Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In relation extraction with distant supervision, noise labels are a bottleneck problem that hinders the performance of training models. Existing neural models solved this problem … |
Hongyan Zhao; Ru Li; Xiaoli Li; Hongye Tan; | Knowl. Based Syst. | 2020-01-01 |
1182 | Intergrating A Minimal Differentiator Expressions Approach Into CBR for Linguistic Pattern Reuse in Crime Relation: Proposed Method Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The relation extraction of crime news can help the monitoring specialists to accelerate the crime investigation. However, constructing patterns or designing templates manually … |
M. Ikhwan Syafiq; M. Shukor Talib; Naomie Salim; Zuriahati Mohd Yunos; Habibollah Haron; | 2020-01-01 | |
1183 | IBRE: An Incremental Bootstrapping Approach for Chinese Appointment and Dismissal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Lu Mao; Meiling Wang; Changliang Li; Junfeng Fan; Changyu Hou; | 2020-01-01 | |
1184 | Research and Application of Chinese Entity Relation Extraction Based on Cyberspace Security Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity relation extraction has played an important role in the construction of semantic knowledge and knowledge graph, Chinese domain entity relation extraction has become more … |
Wangshu Guo; Yejin Tan; Jiawei He; Ming Xian; | 2020 International Conference on Computer Communication and … | 2020-01-01 |
1185 | A Distant Supervised Approach for Relation Extraction in Farsi Texts Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The volume of Farsi information on the Internet has been increasing in recent years. However, most of this information is in the form of unstructured or semi-structured free text. … |
Shireen Atarod; Alireza Yari; | 2020-01-01 | |
1186 | Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge base provides a potential way to improve the intelligence of information retrieval (IR) systems, for that knowledge base has numerous relations between entities which … |
Hai Ye; Zhunchen Luo; | ArXiv | 2020-01-01 |
1187 | Entity Attribute Relation Extraction with Attribute-Aware Embeddings Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines. However, most such knowledge … |
Dan Iter; Xiao Yu; Fangtao Li; | 2020-01-01 | |
1188 | Integrating Text Embedding with Traditional NLP Features for Clinical Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recently, text embedding techniques such as Word2Vec and BERT have produced state-of-the-art results in a wide variety of NLP tasks. As a result, traditional NLP features … |
Fatema Hasan; Arpita Roy; Shimei Pan; | 2020 IEEE 32nd International Conference on Tools with … | 2020-01-01 |
1189 | Active Learning Based Relation Classification for Knowledge Graph Construction from Conversation Data Related Papers Related Patents Related Grants Related Venues Related Experts View |
ZISHAN AHMAD et. al. | 2020-01-01 | |
1190 | Distant Supervised Relation Extraction Via DiSAN-2CNN on A Feature Level Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: At present, the mainstream distant supervised relation extraction methods existed problems: the coarse granularity for coding the context feature information; the difficulty in … |
Xueqiang Lv; Huixin Hou; Xindong You; Xiaopeng Zhang; Junmei Han; | Int. J. Semantic Web Inf. Syst. | 2020-01-01 |
1191 | KEoG: A Knowledge-aware Edge-oriented Graph Neural Network for Document-level Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction (RE) has attracted more and more attentions recently. Edge-oriented graph neural network (EoG) is a new neural network exhibiting greater … |
Tao Li; Weihua Peng; Qingcai Chen; Xiaolong Wang; Buzhou Tang; | 2020 IEEE International Conference on Bioinformatics and … | 2020-01-01 |
1192 | A BERT-based Approach with Relation-aware Attention for Knowledge Base Question Answering IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge Base Question Answering (KBQA), which uses the facts in the knowledge base (KB) to answer natural language questions, has received extensive attention in recent years. … |
Da Luo; Jindian Su; Shanshan Yu; | 2020 International Joint Conference on Neural Networks … | 2020-01-01 |
1193 | Relation Extraction of Minority Cultural Information Resources Based on Bi-LSTM and Double Attention Mechanism Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recent years, the research of minority cultural information resources based on knowledge graph has become a research hotspot in academia. Relation extraction is an integral … |
Lin Cui; Jianhou Gan; Bin Wen; | 2020 IEEE 2nd International Conference on Computer Science … | 2020-01-01 |
1194 | Ontological Relation Classification Using WordNet, Word Embeddings and Deep Neural Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Learning ontological relations is an important step on the way to automatically developing ontologies. This paper introduces a novel way to exploit WordNet [16], the combination … |
Ahlem Chérifa Khadir; Ahmed Guessoum; Hassina Aliane; | 2020-01-01 | |
1195 | An Effective Framework for Document-level Chemical-induced Disease Relation Extraction Via Fine-grained Interaction Between Contexts Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recent years, Chemical-induced Disease (CID) relations are the most searched topics by PubMed users worldwide, reflecting its extensive applications in biomedical research and … |
Jinyong Zhang; Weizhong Zhao; Jincai Yang; Xingpeng Jiang; Tingting He; | 2020 IEEE International Conference on Bioinformatics and … | 2020-01-01 |
1196 | Star-BiLSTM-LAN for Document-level Mutation-Disease Relation Extraction from Biomedical Literature Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relations between mutations and diseases hiding in biomedical literature are valuable for the analysis and interpretation of many complex diseases, which can help explore more … |
YUAN XU et. al. | 2020 IEEE International Conference on Bioinformatics and … | 2020-01-01 |
1197 | Multi Task Learning with General Vector Space for Cross-lingual Semantic Relation Detection Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Semantic relation detection has an important role in natural language processing. In a supervised approach, the training process requires a sufficient amount of labeled data. … |
Rizka W. Sholikah; Agus Z. Arifin; Chastine Fatichah; Ayu Purwarianti; | Journal of King Saud University – Computer and Information … | 2020-01-01 |
1198 | Empirical Exploration of The Challenges in Temporal Relation Extraction from Clinical Text Related Papers Related Patents Related Grants Related Venues Related Experts View |
Diana Galvan-Sosa; Koji Matsuda; Naoaki Okazaki; Kentaro Inui; | Journal of Natural Language Processing | 2020-01-01 |
1199 | Entity Type Enhanced Neural Model for Distantly Supervised Relation Extraction (Student Abstract) Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distantly Supervised Relation Extraction (DSRE) has been widely studied, since it can automatically extract relations from very large corpora. However, existing DSRE methods only … |
Long Bai; Xiaolong Jin; Chuanzhi Zhuang; Xueqi Cheng; | 2020-01-01 | |
1200 | Improving Relation Extraction Using Semantic Role and Multi-task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhandong Zhu; Jindian Su; Xiaobin Hong; | 2020-01-01 | |
1201 | Learning Global Representations for Document-Level Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Lishuang Li; Hongbin Lu; Shuang Qian; Shiyi Zhao; Yifan Zhu; | 2020-01-01 | |
1202 | Research on Relation Extraction Method Based on Multi-channel Convolution and BiLSTM Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Deep learning methods have achieved good results in relation extraction research and have received widespread attention. However, the existing deep learning methods use a single … |
Tao Sun; Dong Wang; | 2020 IEEE Intl Conf on Parallel & Distributed Processing … | 2020-01-01 |
1203 | EDUCATIONAL MULTIMODAL DATA MINING AND FUSION THROUGH KNOWLEDGE GRAPHS FOR TOPIC-RELATION EXTRACTION IN STUDY RECOMMENDATIONS Related Papers Related Patents Related Grants Related Venues Related Experts View |
S. Tomar; H. Abu Rasheed; Madjid Fathi; | 2020-01-01 | |
1204 | Graph Convolution Over Multiple Dependency Sub-graphs for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using … |
Angrosh Mandya; Danushka Bollegala; Frans Coenen; | 2020-01-01 | |
1205 | Chinese Relation Extraction Using Lattice GRU Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Most of the existing Chinese entity relation extraction models have adopted methods which are based on character or word input. However, the word-based input method ignores … |
Can Xu; LiPing Yuan; Yi Zhong; | 2020 IEEE 4th Information Technology, Networking, … | 2020-01-01 |
1206 | Distant Supervised Relation Extraction Model for Reinforcement Learning Combined with Noise Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The distant supervised relation extraction has received wide attention from scholars in recent years. Existing methods for distant supervised relation extraction are based on … |
E Haihong; Xiaosong Zhou; Meina Song; | Proceedings of the 2020 Asia Service Sciences and Software … | 2020-01-01 |
1207 | Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper describes our approach to “DeftEval: Extracting Definitions from Free Text in Textbooks” competition held as a part of Semeval 2020. The task was devoted to finding and … |
Adis Davletov; Nikolay Arefyev; Alexander Shatilov; Denis Gordeev; Alexey Rey; | 2020-01-01 | |
1208 | ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We describe our contribution to two of the subtasks of SemEval 2020 Task 6, DeftEval: Extracting term-definition pairs in free text. The system for Subtask 1: Sentence … |
Fabien Caspani; Pirashanth Ratnamogan; Mathis Linger; Mhamed Hajaiej; | 2020-01-01 | |
1209 | Improving Relation Extraction with Relational Paraphrase Sentences Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Supervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering … |
Junjie Yu; Tong Zhu; Wenliang Chen; Wei Zhang; Min Zhang; | 2020-01-01 | |
1210 | RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multimodal named entity recognition (MNER) for tweets has received increasing attention recently. Most of the multimodal methods used attention mechanisms to capture the … |
LIN SUN et. al. | 2020-01-01 | |
1211 | Combined Networks with Multi-level Attention for Distantly-Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hongyang Yuan; | 2020-01-01 | |
1212 | Spatial Information Extraction from Travel Narratives: Analysing The Notion of Co-occurrence Indicating Closeness of Tourist Places IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recent advancements in social media have generated a myriad of unstructured geospatial data. Travel narratives are among the richest sources of such spatial clues. They are also a … |
Erum Haris; Keng Hoon Gan; Tien-Ping Tan; | Journal of Information Science | 2020-01-01 |
1213 | Research on Relation Extraction of Named Entity on Social Media in Smart Cities Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Social media make significant contribution to the evolution of smart cities. The key issue of smart cities is to develop a series of automatic methods to support smart … |
Zuoguo Liu; Xiaorong Chen; | Soft Computing | 2020-01-01 |
1214 | A Novel Deep Learning Method for Extracting Unspecific Biomedical Relation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Biomedical relation extraction is an important research subject in Natural language processing (NLP). Deep learning technology has shown greater value in improving accuracy of … |
Tian Bai; Chunyu Wang; Ye Wang; Lan Huang; Fuyong Xing; | Concurrency and Computation: Practice and Experience | 2020-01-01 |
1215 | Reducing Wrong Labels for Distantly Supervised Relation Extraction With Reinforcement Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) aims to mine semantic relations between entity pairs from plain texts, which plays an important role in various natural language processing (NLP) tasks. … |
Tiantian Chen; Nianbin Wang; Ming He; Liu Sun; | IEEE Access | 2020-01-01 |
1216 | AHIAP: An Agile Medical Named Entity Recognition and Relation Extraction Framework Based on Active Learning Related Papers Related Patents Related Grants Related Venues Related Experts View |
MING SHENG et. al. | 2020-01-01 | |
1217 | Joint Extraction of Entities and Relations for Chinese Text of Tea Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In view of the problems of polysemy and overlapping relations of Chinese tea text. In this paper, we present a joint model BERT-LCM-Tea for extraction of entities and relations, … |
Zihao Zhou; Wenqian Mu; Xiaoxia Yang; Qi Li; Zhenzhen Chen; | 2020 The 8th International Conference on Information … | 2020-01-01 |
1218 | Adversarial Training Improved Multi-Path Multi-Scale Relation Detector for Knowledge Base Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge Base Question Answering (KBQA) is a promising approach for users to access substantial knowledge and has become a research focus in recent years. Our paper focuses on … |
Yanan Zhang; Guangluan Xu; Xingyu Fu; Li Jin; Tinglei Huang; | IEEE Access | 2020-01-01 |
1219 | An Entity Relation Extraction Algorithm Based on BERT(wwm-ext)-BiGRU-Attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity relation extraction is one of the basic steps of knowledge Graph. It identifies the relations between entities. A BERT-Bidirectional gated recurrent units-Attention … |
RUIHUI HOU et. al. | Proceedings of the 2020 International Conference on … | 2020-01-01 |
1220 | BG-SAC: Entity Relationship Classification Model Based on Self-Attention Supported Capsule Networks IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: To date, deep learning techniques, especially the combination of convolutional neural networks and recurrent neural networks with the attention mechanism, have been the … |
Dunlu Peng; Dongdong Zhang; Cong Liu; Jing Lu; | Appl. Soft Comput. | 2020-01-01 |
1221 | Biomedical Relation Extraction Using Distant Supervision Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: With the accelerating growth of big data, especially in the healthcare area, information extraction is more needed currently than ever, for it can convey unstructured information … |
Nada Boudjellal; Huaping Zhang; Asif Khan; Arshad Ahmad; | Sci. Program. | 2020-01-01 |
1222 | Deep Learning-Based Video Retrieval Using Object Relationships and Associated Audio Classes Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper introduces a video retrieval tool for the 2020 Video Browser Showdown (VBS). The tool enhances the user’s video browsing experience by ensuring full use of video … |
Byoungjun Kim; Ji Yea Shim; Minho Park; Yong Man Ro; | 2020-01-01 | |
1223 | Knowledge-Enhanced Relation Extraction for Chinese EMRs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The growing demand for the meaningful use of electronic medical records has led to great interest in medical entities and relation extraction technologies. Most existing methods … |
Qing Zhao; Jianqiang Li; Chun Xu; Ji-Jiang Yang; Liang Zhao; | IT Professional | 2020-01-01 |
1224 | Span Level Model for The Construction of Scientific Knowledge Graph Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In the recent years, knowledge graphs (KGs) are getting lots of attention due to their wide applications. Constructing KGs involves two major steps, named entity recognition (NER) … |
Ishdutt Trivedi; Sudhan Majhi; | 2020 5th International Conference on Computing, … | 2020-01-01 |
1225 | Big Green at WNUT 2020 Shared Task-1: Relation Extraction As Contextualized Sequence Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation and event extraction is an important task in natural language processing. We introduce a system which uses contextualized knowledge graph completion to classify relations … |
Chris Miller; Soroush Vosoughi; | ArXiv | 2020-01-01 |
1226 | Mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and re- lation recognition over the wet-lab … |
Mohammad Golam Sohrab; Anh-Khoa Duong Nguyen; Makoto Miwa; Hiroya Takamura; | 2020-01-01 | |
1227 | A Targeted Topic Model Based Multi-Label Deep Learning Classification Framework for Aspect-based Opinion Mining Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recently, deep Convolutional Neural Network (CNN) model has achieved remarkable results in Natural Language Processing (NLP) tasks, such as information retrieval, relation … |
THI-CHAM NGUYEN et. al. | 2020 12th International Conference on Knowledge and Systems … | 2020-01-01 |
1228 | Joint Entity-Relation Extraction Via Improved Graph Attention Networks IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint named entity recognition and relation extraction is an essential natural language processing task that aims to identify entities and extract the corresponding relations in … |
Qinghan Lai; Zihan Zhou; Song Liu; | Symmetry | 2020-01-01 |
1229 | Information Extraction with Negative Examples for Author Biographies in Scientific Literatures Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Information extraction (IE) is a textual information processing task concerned with the automatic extraction of entity mentions and relational structures from documents, which has … |
Chuanzhen Li; Youfeng Zeng; Juanjuan Cai; Hui Wang; | 2020 IEEE 4th Information Technology, Networking, … | 2020-01-01 |
1230 | Active Search Using Meta-Bandits Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: There are many applications where positive instances are rare but important to identify. For example, in NLP, positive sentences for a given relation are rare in a large corpus. … |
Shengli Zhu; Jakob Coles; Sihong Xie; | Proceedings of the 29th ACM International Conference on … | 2020-01-01 |
1231 | Attention-Based LSTM with Filter Mechanism for Entity Relation Classification IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is an important research area in the field of natural language processing (NLP), which aims to recognize the relationship between two tagged entities in a … |
Yanliang Jin; Dijia Wu; Weisi Guo; | Symmetry | 2020-01-01 |
1232 | A Neural Relation Extraction Model for Distant Supervision in Counter-Terrorism Scenario Related Papers Related Patents Related Grants Related Venues Related Experts View |
JIAQI HOU et. al. | IEEE Access | 2020-01-01 |
1233 | Chemical Disease Relation Extraction Task Using Genetic Algorithm with Two Novelvoting Methods for Classifier Subset Selection Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Biomedical relation extraction is an important preliminary step for knowledge discovery in the biomedical domain. This paper proposes a multiple classifier system MCS for the … |
Stanley Chika Onye; Nazife Dimililer; Arif Akkeleş; | Turkish J. Electr. Eng. Comput. Sci. | 2020-01-01 |
1234 | Association Rules Extraction Method for Semantic Query Processing Over Medical Big Data Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: With the rapid development of online medical platform, data mining algorithms can effectively deal with an amount of online medical data and solve the complex semantic … |
Ling Wang; Jian Li; Tie Hua Zhou; Wen Qiang Liu; | 2020-01-01 | |
1235 | Learning Relation By Graph Neural Network for SAR Image Few-Shot Learning IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Supervised deep learning models usually need large amounts of labeled data due to the data-driven training strategies, and its applicability to the newly emerging categories that … |
Rui Yang; Xin Xu; Xirong Li; Lei Wang; Fangling Pu; | IGARSS 2020 – 2020 IEEE International Geoscience and Remote … | 2020-01-01 |
1236 | Direction-sensitive Relation Extraction Using Bi-SDP Attention Model IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a crucial task of natural language processing (NLP). It plays a key role in question answering, web search, and information retrieval and so on. Previous … |
Hailin Wang; Ke Qin; Guoming Lu; Guangchun Luo; Guisong Liu; | Knowl. Based Syst. | 2020-01-01 |
1237 | A Dynamic Parameter Enhanced Network for Distant Supervised Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant Supervised Relation Extraction (DSRE) is usually formulated as a problem about classifying a bag of sentences that contains two query entities into the predefined relation … |
Yanjie Gou; Yinjie Lei; Lingqiao Liu; Pingping Zhang; Xi Peng; | Knowl. Based Syst. | 2020-01-01 |
1238 | Relation Extraction Based on Semantic Dependency Graph Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ming Jiang; Jiecheng He; Jianping Wu; Chengjie Qi; Min Zhang; | J. Comput. Methods Sci. Eng. | 2020-01-01 |
1239 | Review of Entity Relation Extraction Methods IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Li Dongmei; Zhang Yang; Li Dongyuan; Lin Danqiong; | Journal of Computer Research and Development | 2020-01-01 |
1240 | A Span-based Model for Joint Entity and Relation Extraction with Relational Graphs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting related entity pairs from natural language texts is essential to constructing knowledge graphs. However, prior work is limited by the long distance between entities and … |
Xingang Wang; Dong Wang; Fengpo Ji; | 2020 IEEE Intl Conf on Parallel & Distributed Processing … | 2020-01-01 |
1241 | RUREBUS-2020 SHARED TASK: RUSSIAN RELATION EXTRACTION FOR BUSINESS Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we present a shared task on core information extraction problems, named entity recognition and relation extraction. In contrast to popular shared tasks on related … |
V. A. IVANIN et. al. | 2020-01-01 | |
1242 | Structural Block Driven Enhanced Convolutional Neural Representation for Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we propose a novel lightweight relation extraction approach of structural block driven convolutional neural learning. Specifically, we detect the essential … |
Dongsheng Wang; Prayag Tiwari; Sahil Garg; Hongyin Zhu; Peter Bruza; | Appl. Soft Comput. | 2020-01-01 |
1243 | Joint Entity Linking and Relation Extraction with Neural Networks for Knowledge Base Population Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction and entity linking are two fundamental procedures to extend knowledge bases. Most existing methods typically treat them separately and ignore the semantic … |
Zhenyu Zhang; Xiaobo Shu; Tingwen Liu; Zheng Fang; Quangang Li; | 2020 International Joint Conference on Neural Networks … | 2020-01-01 |
1244 | A Hybrid Model with Pre-trained Entity-Aware Transformer for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distantly supervised relation extraction is an efficient method to extract novel relational facts from unstructed text. Most previous neural methods adopt Convolutional Neural … |
Jinxin Yao; Min Zhang; Biyang Wang; Xianda Xu; | 2020-01-01 | |
1245 | Edge Features Enhanced Graph Attention Network for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Dependency trees of sentences contain much structural information that is useful for capturing long-range relations between words in the text. In order to distill the useless … |
Xuefeng Bai; Chong Feng; Huanhuan Zhang; Xiaomei Wang; | 2020-01-01 | |
1246 | Graph-based Bayesian Meta Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Meta-learning methods accomplish rapid adaptation to a new task using few samples by first learning an internal representation that matches with similar tasks. In this paper, we … |
Zhen Wang; Zhenting Zhang; | 2020 16th International Conference on Computational … | 2020-01-01 |
1247 | UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Definition Extraction is the task to automatically extract terms and their definitions from text. In recent years, it attracts wide interest from NLP researchers. This paper … |
SHUYI XIE et. al. | 2020-01-01 | |
1248 | Graph Enhanced Dual Attention Network for Document-Level Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relational facts. To improve … |
BO LI et. al. | 2020-01-01 | |
1249 | Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment … |
Yanguang Chen; Yuanyuan Sun; Zhihao Yang; Hongfei Lin; | 2020-01-01 | |
1250 | A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a … |
Haopeng Ren; Yi Cai; Xiaofeng Chen; Guohua Wang; Qing Li; | 2020-01-01 | |
1251 | Meta-Information Guided Meta-Learning for Few-Shot Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to … |
BOWEN DONG et. al. | 2020-01-01 | |
1252 | ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling … |
Erxin Yu; Wenjuan Han; Yuan Tian; Yi Chang; | 2020-01-01 | |
1253 | Document-level Relation Extraction with Dual-tier Heterogeneous Graph Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the … |
ZHENYU ZHANG et. al. | 2020-01-01 | |
1254 | Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Dependency trees have been shown to be effective in capturing long-range relations between target entities. Nevertheless, how to selectively emphasize target-relevant information … |
BOWEN YU et. al. | 2020-01-01 | |
1255 | Using A Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We tackle implicit discourse relation classification, a task of automatically determining semantic relationships between arguments. The attention-worthy words in arguments are … |
Xiao Li; Yu Hong; Huibin Ruan; Zhen Huang; | 2020-01-01 | |
1256 | Event-Guided Denoising for Multilingual Relation Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that … |
Amith Ananthram; Emily Allaway; Kathleen McKeown; | 2020-01-01 | |
1257 | Co-Attention Based Few-Shot Relation Classification Model with Dynamic Routing Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: With the development of natural neural networks, supervised methods are usually confronted with the problem of lacking labeled data. Few-shot learning methods are now a mainstream … |
CHUN HUANG et. al. | 2020 IEEE 3rd International Conference on Information … | 2020-01-01 |
1258 | High-Precision Biomedical Relation Extraction for Reducing Human Curation Efforts in Industrial Applications Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The body of biomedical literature is growing at an unprecedented rate, exceeding the ability of researchers to make effective use of this knowledge-rich amount of information. … |
Alan Ramponi; Stefano Giampiccolo; Danilo Tomasoni; Corrado Priami; Rosario Lombardo; | IEEE Access | 2020-01-01 |
1259 | BERT-Based Chinese Relation Extraction for Public Security IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The past few years have witnessed some public safety incidents occurring around the world. With the advent of the big data era, effectively extracting public security information … |
JIAQI HOU et. al. | IEEE Access | 2020-01-01 |
1260 | LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We present a system which allows a user to create event-event relation extractors on-demand with a small amount of effort. The system provides a suite of algorithms, flexible … |
Bonan Min; Manaj Srivastava; Haoling Qiu; Prasannakumar Muthukumar; Joshua Fasching; | 2020-01-01 | |
1261 | DSREFC: Improving Distantly-supervised Neural Relation Extraction Using Feature Combination Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervisory relationship extraction can automatically align the expected entity pairs, and automatically obtain a large number of annotation data, thus saving a lot of … |
Jibin He; Yawei Zhao; Gang Luo; | Proceedings of the 2020 12th International Conference on … | 2020-01-01 |
1262 | Relation Extraction Across Sentences Using Bi-directional Long Short Term Memory Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Most of the past work on relation extraction(RE) has focused on identifying relationships between entities within a sentence. Nowadays, most of the research in the field of RE has … |
C A Deepa; P C Reghu Raj; Ajeesh Ramanujan; | 2020 International Conference on Emerging Trends in … | 2020-01-01 |
1263 | DeSpin: A Prototype System for Detecting Spin in Biomedical Publications Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Improving the quality of medical research reporting is crucial to reduce avoidable waste in research and to improve the quality of health care. Despite various initiatives aiming … |
Anna Koroleva; Sanjay Kamath; Patrick Bossuyt; Patrick Paroubek; | 2020-01-01 | |
1264 | A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text. However, the current approach is … |
CHEN LIN et. al. | 2020-01-01 | |
1265 | A Comprehensive Exploration of Semantic Relation Extraction Via Pre-trained CNNs IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Semantic relation extraction between entity pairs is a crucial task in information extraction from text. In this paper, we propose a new pre-trained network architecture for this … |
Qing Li; Lili Li; Weinan Wang; Qi Li; Jiang Zhong; | Knowl. Based Syst. | 2020-01-01 |
1266 | Relation Classification Via Knowledge Graph Enhanced Transformer Encoder IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is an important task in natural language processing fields. The goal is to predict predefined relations for the marked nominal pairs in given sentences. … |
Wenti Huang; Yiyu Mao; Zhan Yang; Lei Zhu; Jun Long; | Knowl. Based Syst. | 2020-01-01 |
1267 | A Semantic Framework for Extracting Taxonomic Relations from Text Corpus Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Nowadays, ontologies have been exploited in many current applications due to the abilities in representing knowledge and inferring new knowledge. However, the manual construction … |
Phuoc Thi Hong Doan; Ngamnij Arch-int; Somjit Arch-int; | Int. Arab J. Inf. Technol. | 2020-01-01 |
1268 | A Noise Adaptive Model for Distantly Supervised Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is an important task in natural language processing. To obtain a large amount of annotated data, distant supervision is introduced by using large-scale … |
Xu Huang; Bowen Zhang; Yunming Ye; Xiaojun Chen; Xutao Li; | 2020-01-01 | |
1269 | Research on Visual Relation Detection Based on Computer Vision Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting semantic information from unstructured data, such as images or text, is a critical challenge in artificial intelligence and a long-lasting research direction. In … |
Mengyang Liu; Hongjuan Wang; Yeli Li; Yuning Bian; | 2020 3rd International Conference on Advanced Electronic … | 2020-01-01 |
1270 | Joint Model of Entity Recognition and Relation Extraction with Self-attention Mechanism IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recent years, the joint model of entity recognition (ER) and relation extraction (RE) has attracted more and more attention in the healthcare and medical domains. However, … |
Maofu Liu; Yukun Zhang; Wenjie Li; Dong-Hong Ji; | ACM Transactions on Asian and Low-Resource Language … | 2020-01-01 |
1271 | CEREX@FIRE-2020: Overview of The Shared Task on Cause-effect Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extraction of causal relations from text is an important problem in Natural Language Processing (NLP). The extracted relations play important roles in several downstream … |
Manjira Sinha; Tirthankar Dasgupta; Lipika Dey; | Forum for Information Retrieval Evaluation | 2020-01-01 |
1272 | Pairwise Causality Structure: Towards Nested Causality Mining on Financial Statements Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Causality mining, which aims to find cause-effect relations in text, is an important yet challenging problem in natural language understanding. The extraction of causal relations … |
Dian Chen; Yixuan Cao; Ping Luo; | 2020-01-01 | |
1273 | Incorporating Temporal Cues and AC-GCN to Improve Temporal Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Temporal relation classification, an important branch of relation extraction, aims to identify the time sequence among events. Currently, Shortest Dependency Path (SDP) is widely … |
Xinyu Zhou; Peifeng Li; Qiaoming Zhu; Fang Kong; | 2020-01-01 | |
1274 | Relation Extraction with BERT-based Pre-trained Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision relation extraction is an effective method to extract the real relation between entities from unstructured corpus. However, affected by the hypothesis of … |
HAITAO YU et. al. | 2020 International Wireless Communications and Mobile … | 2020-01-01 |
1275 | Auxiliary Learning for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
SHENGFEI LYU et. al. | 2020-01-01 | |
1276 | Single Concatenated Input Is Better Than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Chemical compounds (drugs) and diseases are among top searched keywords on the PubMed database of biomedical literature by biomedical researchers all over the world (according to … |
Pham Thi Quynh Trang; Bui Manh Thang; Dang Thanh Hai; | VNU Journal of Science: Computer Science and Communication … | 2020-01-01 |
1277 | Can We Survive Without Labelled Data in NLP? Transfer Learning for Open Information Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Various tasks in natural language processing (NLP) suffer from lack of labelled training data, which deep neural networks are hungry for. In this paper, we relied upon features … |
Injy Sarhan; Marco Spruit; | Applied Sciences | 2020-01-01 |
1278 | Improving Relation Extraction By Multi-task Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a subtask of information extraction. Current relation extraction methods are mainly designed for relation extraction tasks, and they use limited knowledge. … |
Weijie Wang; Wenxin Hu; | Proceedings of the 2020 4th High Performance Computing and … | 2020-01-01 |
1279 | RST Discourse Parser for Russian: An Experimental Study of Deep Learning Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This work presents the first fully-fledged discourse parser for Russian based on the Rhetorical Structure Theory of Mann and Thompson (1988). For the segmentation, discourse tree … |
ELENA CHISTOVA et. al. | 2020-01-01 | |
1280 | Building Knowledge Graph Using Pre-trained Language Model for Learning Entity-aware Relationships Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relations exhibited among entities from textual content can be a potential source of information for any business domain. This paper encompasses a wholesome approach to mine … |
Abhijeet Kumar; Abhishek Pandey; Rohit Gadia; Mridul Mishra; | 2020 IEEE International Conference on Computing, Power and … | 2020-01-01 |
1281 | A Survey on Neural Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a key task for knowledge graph construction and natural language processing, which aims to extract meaningful relational information between entities from … |
Kang Liu; | Science China-technological Sciences | 2020-01-01 |
1282 | Distant Supervision Relation Extraction Model Based on Feature-recalibration Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) is a crucial ingredient in numerous natural language processing tasks for mining structured facts from heterogeneous texts. This paper presents a novel … |
Tianji Chang; Wu Yang; Qingmin Liang; Yue Wang; | 2020 15th IEEE Conference on Industrial Electronics and … | 2020-01-01 |
1283 | Capsule Networks With Word-Attention Dynamic Routing for Cultural Relics Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Online museums and online cultural relic information provide abundant data for relation extraction research. However, in the relation extraction task of modelling space … |
Min Zhang; Guohua Geng; | IEEE Access | 2020-01-01 |
1284 | Relative and Incomplete Time Expression Anchoring for Clinical Text Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in … |
Louise Dupuis; Nicol Bergou; Hegler Tissot; Sumithra Velupillai; | 2020-01-01 | |
1285 | Finding Out Noisy Patterns for Relation Extraction of Bangla Sentences Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually … |
Rukaiya Habib; Md. Musfique Anwar; | 2020-01-01 | |
1286 | Extracting Relations Between Radiotherapy Treatment Details Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records. Radiotherapy is a central component of the … |
DANIELLE BITTERMAN et. al. | 2020-01-01 | |
1287 | Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In drug development, protocols define how clinical trials are conducted, and are therefore of paramount importance. They contain key patient-, investigator-, medication-, and … |
Miao Chen; Ganhui Lan; Fang Du; Victor Lobanov; | 2020-01-01 | |
1288 | QA4IE: A Question Answering Based System for Document-Level General Information Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Information Extraction (IE) is the task of distilling structured information from unstructured texts by identifying references to named entities as well as relationships between … |
Lin Qiu; Dongyu Ru; Quanyu Long; Weinan Zhang; Yong Yu; | IEEE Access | 2020-01-01 |
1289 | DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process … |
SungMin Yang; SoYeop Yoo; OkRan Jeong; | Applied Sciences | 2020-01-01 |
1290 | A Multi-feature Fusion Model for Chinese Relation Extraction with Entity Sense IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is an important task of information extraction. Most existing methods of Chinese language relation extraction are based on word input. They are highly … |
JIANGYING ZHANG et. al. | Knowl. Based Syst. | 2020-01-01 |
1291 | Deep Embedding for Relation Extraction on Insufficient Labelled Data Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Many recently proposed relation extraction methods are based on distantly supervised learning. They use data from existing knowledge bases as training data. Although the methods … |
Haojie Huang; Raymond K. Wong; | 2020 International Joint Conference on Neural Networks … | 2020-01-01 |
1292 | AGCN: Attention-based Graph Convolutional Networks for Drug-drug Interaction Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting drug-drug interaction (DDI) relations is one of the most typical tasks in the field of biomedical relation extraction. Automatic DDI extraction from the biomedical … |
Chanhee Park; Jinuk Park; Sanghyun Park; | Expert Syst. Appl. | 2020-01-01 |
1293 | An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information … |
Xiaoyu Han; Yue Zhang; Wenkai Zhang; Tinglei Huang; | Inf. | 2020-01-01 |
1294 | Semantically Enhanced and Minimally Supervised Models for Ontology Construction, Text Classification, and Document Recommendation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The proliferation of deliverable knowledge on the web, along with the rapidly increasing number of accessible research publications, make researchers, students, and educators … |
Wael Alkhatib; | 2020-01-01 | |
1295 | Joint Extraction of Entities and Relations By A Novel End-to-end Model with A Double-pointer Module IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint extraction of entities and relations is to detect entities and recognize semantic relations simultaneously. However, some existing joint models predict relations on words, … |
Chongyou Bai; Limin Pan; Senlin Luo; Zhouting Wu; | Neurocomputing | 2020-01-01 |
1296 | Utber: Utilizing Fine-Grained Entity Types to Relation Extraction with Distant Supervision Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recently, much effort has been paid to relation extraction during the construction of large ontological knowledge bases (KBs). However, most of the traditional relation extraction … |
Chengmin Wu; Lei Chen; | 2020 IEEE International Conference on Smart Data Services … | 2020-01-01 |
1297 | A Multi-Channel Deep Neural Network for Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The task of relation recognition identifies semantic relationships between two named entities in a sentence. In neural network based models, a convolutional layer is often … |
YANPING CHEN et. al. | IEEE Access | 2020-01-01 |
1298 | Cross-sentence N-ary Relation Classification Using LSTMs on Graph and Sequence Structures Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is an important semantic processing task in the field of Natural Language Processing (NLP). The past works mainly focused on binary relations in a single … |
Lulu Zhao; Weiran Xu; Sheng Gao; Jun Guo; | Knowl. Based Syst. | 2020-01-01 |
1299 | GREG: A Global Level Relation Extraction with Knowledge Graph Embedding IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules … |
Kuekyeng Kim; YunA Hur; Gyeongmin Kim; Heuiseok Lim; | Applied Sciences | 2020-01-01 |
1300 | A Survey Deep Learning Based Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiaxia Zhang; Yugang Dai; Tao Jiang; | 2020-01-01 | |
1301 | Relation Extraction and Visualization Using Natural Language Processing Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper discusses relationship extraction among actors/nodes in the text provided. Given a text data, relationships are extracted using natural language processing and shown in … |
B. K. Uday; Kailash Gogineni; Akhil Chitreddy; P. Natarajan; | 2020-01-01 | |
1302 | Causal Knowledge Extraction Through Large-Scale Text Mining IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection … |
OKTIE HASSANZADEH et. al. | 2020-01-01 | |
1303 | Attention-based Bidirectional Long Short-Term Memory Networks for Relation Classification Using Knowledge Distillation from BERT IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is an important task in the field of natural language processing. Today the best-performing models often use huge, transformer-based neural architectures … |
Zihan Wang; Bo Yang; | 2020 IEEE Intl Conf on Dependable, Autonomic and Secure … | 2020-01-01 |
1304 | Relation Extraction for Chinese Clinical Records Using Multi-View Graph Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a necessary step in obtaining information from clinical medical records. In the medical domain, there have been several studies on relation extraction in … |
Chunyang Ruan; Yingpei Wu; Guang Sheng Luo; Yun Yang; Pingchuan Ma; | IEEE Access | 2020-01-01 |
1305 | Relation Extraction Based on Fusion Dependency Parsing from Chinese EMRs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The Electronic Medical Record (EMR) contains a great deal of medical knowledge related to patients, which has been widely used in the construction of medical knowledge graphs. … |
Pengjun Zhai; Xin Huang; Beibei Zhang; Yu Fang; | Sci. Program. | 2020-01-01 |
1306 | BGSGA: Combining Bi-GRU and Syntactic Graph Attention for Improving Distant Supervision Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision Relation Extraction(RE) aligns entities in a Knowledge Base (KB) with text to automatically construct large-labeled corpus, which alleviates the need for … |
Chengcheng Peng; Bin Wu; Zekun Li; | Proceedings of the 2020 9th International Conference on … | 2020-01-01 |
1307 | Person-relation Extraction Using Bert Based Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View |
S M Yang; S Y Yoo; Y S Ahn; Ok-Ran Jeong; | 2020-01-01 | |
1308 | Automatic Detection of Important Tokens on Dependency Trees for Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
Tomoki Tsujimura; Makoto Miwa; Yutaka Sasaki; | Journal of Natural Language Processing | 2020-01-01 |
1309 | A Multi-Task Learning Neural Network for Emotion-Cause Pair Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Emotion-cause pair extraction, which aims at extracting both the emotion and its corresponding cause in text, is a significant and challenging task in emotion analysis. Previous … |
Sixing Wu; Fang Chen; Yongfeng Huang; Xing Li; | 2020-01-01 | |
1310 | DAM: Transformer-based Relation Detection for Question Answering Over Knowledge Base IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Detection is a core component of Knowledge Base Question Answering (KBQA). In this paper, we propose a Transformer-based deep attentive semantic matching model (DAM), to … |
Yongrui Chen; Huiying Li; | Knowl. Based Syst. | 2020-01-01 |
1311 | Interactive Learning for Joint Event and Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We tackle the problems of both event and entity relation extraction, and come up with a novel method to implement joint extraction: iteratively interactive learning. This method … |
Jingli Zhang; Yu Hong; Wenxuan Zhou; Jianmin Yao; Min Zhang; | International Journal of Machine Learning and Cybernetics | 2020-01-01 |
1312 | Measuring Triplet Trustworthiness in Knowledge Graphs Via Expanded Relation Detection Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Nowadays, large scale knowledge graphs are usually constructed by (semi-)automatic information extraction methods. Nevertheless, the technology is not perfect, because it cannot … |
Aibo Guo; Zhen Tan; Xiang Zhao; | 2020-01-01 | |
1313 | Joint Learning for Document-Level Threat Intelligence Relation Extraction and Coreference Resolution Based on GCN Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In order to help researchers quickly understand the connection between new threat events and previous threat events, threat intelligence document-level relation extraction plays a … |
XUREN WANG et. al. | 2020 IEEE 19th International Conference on Trust, Security … | 2020-01-01 |
1314 | Scene Graph Generation Via Multi-relation Classification and Cross-modal Attention Coordinator Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Scene graph generation intends to build graph-based representation from images, where nodes and edges respectively represent objects and relationships between them. However, scene … |
Xiaoyi Zhang; Zheng Wang; Xing Xu; Jiwei Wei; Yang Yang; | Proceedings of the 2nd ACM International Conference on … | 2020-01-01 |
1315 | Relation Extraction Using Language Model Based on Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View |
Chengli Xing; Xueyang Liu; Dongdong Du; Wenhui Hu; Minghui Zhang; | 2020-01-01 | |
1316 | Partial Domain Adaptation for Relation Extraction Based on Adversarial Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction methods based on domain adaptation have begun to be extensively applied in specific domains to alleviate the pressure of insufficient annotated corpus, which … |
Xiaofei Cao; Juan Yang; Xiangbin Meng; | The Semantic Web | 2020-01-01 |
1317 | Selective Self-attention with BIO Embeddings for Distant Supervision Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision method is proposed to label instances automatically, which could operate relation extraction without human annotations. However, the training data generated in … |
Mingjie Tang; Bo Yang; | 2020-01-01 | |
1318 | Dynamic Prototype Selection By Fusing Attention Mechanism for Few-Shot Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In a relation classification task, few-shot learning is an effective method when the number of training instances decreases. The prototypical network is a few-shot classification … |
Linfang Wu; Hua-Ping Zhang; Yaofei Yang; Xin Liu; Kai Gao; | 2020-01-01 | |
1319 | Dynamic Relation Extraction with A Learnable Temporal Encoding Method Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The need to judge the relations between two entities at a specific time arises in many natural language understanding and knowledge graph related tasks, where the traditional … |
Yinghan Shen; Xuhui Jiang; Yuanzhuo Wang; Xiaolong Jin; Xueqi Cheng; | 2020 IEEE International Conference on Knowledge Graph (ICKG) | 2020-01-01 |
1320 | Siamese BERT Model with Adversarial Training for Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is a very important Natural Language Processing (NLP) task to classify the relations from the plain text. It is one of the basic tasks of constructing a … |
ZHIMIN LIN et. al. | 2020 IEEE International Conference on Knowledge Graph (ICKG) | 2020-01-01 |
1321 | Semantic Relation Extraction Using Sequential and Tree-structured LSTM with Attention IF:4 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Semantic relation extraction is crucial to automatically constructing a knowledge graph (KG), and it supports a variety of downstream natural language processing (NLP) tasks such … |
Zhiqiang Geng; GuoFei Chen; Yongming Han; Gang Lu; Fang Li; | Inf. Sci. | 2020-01-01 |
1322 | Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we propose a new few-shot text classification method. Compared with supervised learning methods which require a large corpus of labeled documents, our method aims … |
Chen Lyu; Weijie Liu; Ping Wang; | 2020-01-01 | |
1323 | A Weighted GCN with Logical Adjacency Matrix for Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Graph convolutional network (GCN), with its capability to update the current node features according to the features of its first-order adjacent nodes and edges, has achieved … |
Li Zhou; Tingyu Wang; Hong Qu; Li Huang; Yuguo Liu; | 2020-01-01 | |
1324 | Chinese Open Relation Extraction with Pointer-Generator Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Most of the traditional Chinese open relation extraction (Open RE) system exploit the syntactic, lexical and other language structure information obtained by natural language … |
Ziheng Cheng; Xu Wu; Xiaqing Xie; Jingchen Wu; | 2020 IEEE Fifth International Conference on Data Science in … | 2020-01-01 |
1325 | Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we present a novel end-to-end neural model based on graph convolutional networks (GCN) for jointly extracting entities and relations between them. It divides the … |
YIN HONG et. al. | IEEE Access | 2020-01-01 |
1326 | Knowledge Graph Generation with Deep Active Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Creating a knowledge graph automatically from raw unstructured text has always been a job of domain expert which takes months to curate and refine. In this paper, we propose a … |
Abhishek Pradhan; Ketan Kumar Todi; Anbarasan Selvarasu; Atish Sanyal; | 2020 International Joint Conference on Neural Networks … | 2020-01-01 |
1327 | Ontology Learning Pada Teks Tidak Terstruktur Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Informasi yang tersebar pada berbagai sumber di internet banyak ditujukan hanya untuk manusia saja. Sementara itu, muncul kebutuhan agar informasi tersebut tidak hanya bisa dibaca … |
Rajif Agung Yunmar; | The Electrician | 2020-01-01 |
1328 | Cross-Modal Representation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Cross-modal representation learning is an essential part of representation learning, which aims to learn latent semantic representations for modalities including texts, audio, … |
Zhiyuan Liu; Yankai Lin; Maosong Sun; | 2020-01-01 | |
1329 | Chemical-induced Disease Relation Extraction With Dependency Information And Prior Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel convolutional attention network (CAN) for CDR extraction. |
HUIWEI ZHOU et. al. | arxiv-cs.CL | 2020-01-01 |
1330 | Eliciting Knowledge from Language Models Using Automatically Generated Prompts Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as … |
Taylor Shin; Yasaman Razeghi; IV RobertL.Logan; Eric Wallace; Sameer Singh; | ArXiv | 2020-01-01 |
1331 | BERT-based Spatial Information Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Spatial information extraction is essential to understand geographical information in text. This task is largely divided to two subtasks: spatial element extraction and spatial … |
Hyeong Jin Shin; Jeong Yeon Park; Dae Bum Yuk; Jae Sung Lee; | 2020-01-01 | |
1332 | Mutual Relation Detection for Complex Question Answering Over Knowledge Graph Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Question Answering over Knowledge Graph (KG-QA) becomes a convenient way to interact with the prevailing information. The user’s information needs, i.e., input questions become … |
Qifan Zhang; Peihao Tong; Junjie Yao; Xiaoling Wang; | 2020-01-01 | |
1333 | Relation Extraction with Proactive Domain Adaptation Strategy Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is an important information extraction task in many Natural Language Processing (NLP) applications, such as automatic knowledge graph construction, question … |
Lingfeng Zhong; Yi Zhu; | 2020 IEEE International Conference on Knowledge Graph (ICKG) | 2020-01-01 |
1334 | Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in The Construction of Military Equipment Knowledge Graph IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In the field of military research, manufacturing and management of weapons and equipment are very important. Due to the continuous advancement of science and technology, many … |
Chenguang Liu; Yongli Yu; Xingxin Li; Peng Wang; | IEEE Access | 2020-01-01 |
1335 | Curriculum Learning for Distant Supervision Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction under distant supervision leverages the existing knowledge base to label data automatically, thus greatly reduced the consumption of human labors. Although … |
Qiongxin Liu; Peng Wang; Jiasheng Wang; Jing Ma; | J. Web Semant. | 2020-01-01 |
1336 | Improving Document-level Relation Extraction Via Contextualizing Mention Representations AndWeighting Mention Pairs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction (RE) has attracted considerable attention, because a large number of relational facts are expressed in multiple sentences. Recently, … |
Ping Jiang; Xian-Ling Mao; Bin-Bin Bian; Heyan Huang; | 2020 IEEE International Conference on Knowledge Graph (ICKG) | 2020-01-01 |
1337 | Context and Type Enhanced Representation Learning for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction from plain text aims to extract relational facts between entities in the text, and plays an important role in knowledge graph construction, question answering, … |
Erxin Yu; Yantao Jia; Shang Wang; Fengfu Li; Yi Chang; | 2020 IEEE International Conference on Knowledge Graph (ICKG) | 2020-01-01 |
1338 | A Two-Level Noise-Tolerant Model for Relation Extraction with Reinforcement Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision has been widely used to automatically label data for relation extraction, but inevitably suffers from wrong labeling problems. Existing methods solve the noisy … |
Erxin Yu; Yantao Jia; Yuan Tian; Yi Chang; | 2020 IEEE International Conference on Knowledge Graph (ICKG) | 2020-01-01 |
1339 | Biomedical Event Extraction As Multi-turn Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Biomedical event extraction from natural text is a challenging task as it searches for complex and often nested structures describing specific relationships between multiple … |
Xing David Wang; Leon Weber; Ulf Leser; | 2020-01-01 | |
1340 | Event Detection Based on Open Information Extraction and Ontology Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a … |
Sihem Sahnoun; Samir Elloumi; Sadok Ben Yahia; | Journal of Information and Telecommunication | 2020-01-01 |
1341 | Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph … |
HUIWEI ZHOU et. al. | 2020-01-01 | |
1342 | Research on Progress and Inspiration of Entity Relation Extraction in English Open Domain Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jian Xu; Xianming Yao; Jianhou Gan; Yu Sun; | 2020-01-01 | |
1343 | Hamming Distance Encoding Multihop Relation Knowledge Graph Completion Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge graphs (KGs) play an important role in many real-world applications like information retrieval, question answering, relation extraction, etc. To reveal implicit … |
Panfeng Chen; Yisong Wang; Quan Yu; Yi Fan; Renyan Feng; | IEEE Access | 2020-01-01 |
1344 | Research on Entity Relation Extraction in Education Field Based on Multi-feature Deep Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is the core task of information extraction and natural language processing tasks. Most of the existing methods stop at extracting the context characteristics … |
Mengxue Song; Jingsheng Zhao; Xiang Gao; | Proceedings of the 2020 3rd International Conference on Big … | 2020-01-01 |
1345 | A Survey Study on Relation Extraction for Web Pages Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Natural language means a language that is used for communication by human. Natural Language Processing (NLP) helps machines to understand the natural language. The natural … |
Ghada Alsaigh; Ghayda Al-Talib; Alaa Y. Taqa; | Journal of Education Science | 2020-01-01 |
1346 | Modelling Relations with Prototypes for Visual Relation Detection Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relations between objects drive our understanding of images. Modelling them poses several challenges due to the combinatorial nature of the problem and the complex structure of … |
François Plesse; Alexandru Ginsca; Bertrand Delezoide; Françoise Prêteux; | Multimedia Tools and Applications | 2020-01-01 |
1347 | Semantic Relation Detection Based on Multi-task Learning and Cross-Lingual-View Embedding Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Semantic relation extraction automatically is an important task in NLP. Various methods have been developed using either pattern-based approach or distributional approach. … |
Rizka Wakhidatus Sholikah; Agus Arifin; Chastine Fatichah; Ayu Purwarianti; | International Journal of Intelligent Engineering and Systems | 2020-01-01 |
1348 | Domain Relation Extraction from Noisy Chinese Texts Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a typical method to extend the knowledge graph (KG); nevertheless, when it is applied in a particular domain, the issue of text sparsity becomes noteworthy. … |
Ning Pang; Zhen Tan; Xiang Zhao; Weixin Zeng; Weidong Xiao; | Neurocomputing | 2020-01-01 |
1349 | Classification of Medical Images Based on Mixed Attributes Relation Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hyung-Il Kim; Hyun-Nim Yoon; | 2020-01-01 | |
1350 | Research on Extraction of Human Information Entity Relationship Based on Improved Capsule Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity relation extraction is to learn the implicit semantic relations among entities from multiple entities of a single sentence. Extracting entity relationships from … |
Lige Yang; Liping Zheng; Lijuan Zheng; | 2020 International Workshop on Electronic Communication and … | 2020-01-01 |
1351 | Large-scale Relation Extraction from Web Documents and Knowledge Graphs with Human-in-the-loop Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The Semantic Web movement has produced a wealth of curated collections of entities and facts, often referred as Knowledge Graphs. Creating and maintaining such Knowledge Graphs is … |
Petar Ristoski; Anna Lisa Gentile; Alfredo Alba; Daniel Gruhl; Steve Welch; | J. Web Semant. | 2020-01-01 |
1352 | FAT-RE: A Faster Dependency-free Model for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recent years have seen the dependency tree as effective information for relation extraction. Two problems still exist in previous methods: (1) dependency tree relies on external … |
Lifang Ding; Zeyang Lei; Guangxu Xun; Yujiu Yang; | J. Web Semant. | 2020-01-01 |
1353 | Improving Relation Classification By Incorporating Dependency and Semantic Information Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is the task of identifying relations between two entities in a sentence, which is an essential step in the standard NLP pipeline. Most of the previous … |
Kun Deng; Shaochun Wu; | 2020 International Joint Conference on Neural Networks … | 2020-01-01 |
1354 | A Cross-Media Deep Relationship Classification Method Using Discrimination Information Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification is one of the most fundamental tasks in the area of cross-media, which is essential for many practical applications such as information extraction, … |
Weifeng Hu; Baosen Ma; Zeqiang Li; Yujun Li; Yue Wang; | Inf. Process. Manag. | 2020-01-01 |
1355 | A Semi-supervised Joint Entity and Relation Extraction Model Based on Tagging Scheme and Information Gain Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint entity and relation extraction, completing the entity recognition and relation extraction simultaneously, can better integrate the information between two tasks and reduce … |
YONGLIN ZHAO et. al. | 2020-01-01 | |
1356 | Targeting Precision: A Hybrid Scientific Relation Extraction Pipeline for Improved Scholarly Knowledge Organization Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Knowledge graphs have been successfully built from unstructured texts in general domains such as newswire by leveraging distant supervision relation signals from linked data … |
Ming Jiang; Jennifer D’Souza; Sören Auer; J. Stephen Downie; | Proceedings of the Association for Information Science and … | 2020-01-01 |
1357 | Improve Relation Extraction with Dual Attention-guided Graph Convolutional Networks IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: To better learn the dependency relationship between nodes, we address the relationship extraction task by capturing rich contextual dependencies based on the attention mechanism, … |
ZHIXIN LI et. al. | Neural Computing and Applications | 2020-01-01 |
1358 | Entity Relative Position Representation Based Multi-head Selection for Joint Entity and Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint entity and relation extraction has received increasing interests recently, due to the capability of utilizing the interactions between both steps. Among existing studies, … |
Tianyang Zhao; Zhao Yan; Yunbo Cao; Zhoujun Li; | 2020-01-01 | |
1359 | Joint Extraction of Entities and Relations Using Graph Convolution Over Pruned Dependency Trees IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We present a novel end-to-end deep neural network model based on graph convolutional networks for simultaneous joint extraction of entities and relations among them. Our model … |
Yin Hong; Yanxia Liu; Suizhu Yang; Kaiwen Zhang; Jianjun Hu; | Neurocomputing | 2020-01-01 |
1360 | File Training Generator For Indonesian Language In Named Entity Recognition Using Anago Library Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Named Entity Recognition (NER) or Named Entity Recognition and Classification (NERC) is one of the main components of an information extraction task that aims to detect and … |
IRFAN FADIL et. al. | 2020-01-01 | |
1361 | Boundaries and Edges Rethinking: An End-to-end Neural Model for Overlapping Entity Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Overlapping entity relation extraction has received extensive research attention in recent years. However, existing methods suffer from the limitation of long-distance … |
Hao Fei; Yafeng Ren; Donghong Ji; | Inf. Process. Manag. | 2020-01-01 |
1362 | Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on A Hybrid Method Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and … |
Ayiguli Halike; Kahaerjiang Abiderexiti; Tuergen Yibulayin; | Inf. | 2020-01-01 |
1363 | Extracting Temporal and Causal Relations Based on Event Networks IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have … |
Duc-Thuan Vo; Feras Al-Obeidat; Ebrahim Bagheri; | Inf. Process. Manag. | 2020-01-01 |
1364 | Noise Reduction Framework for Distantly Supervised Relation Extraction with Human in The Loop Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision is a widely used data labeling method for relation extraction. While aligning knowledge base with the corpus, distant supervision leads to a mass of wrong … |
Xinyuan Zhang; Hongzhi Liu; Zhonghai Wu; | 2020 IEEE 10th International Conference on Electronics … | 2020-01-01 |
1365 | Optimization of Hierarchical Reinforcement Learning Relationship Extraction Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between … |
Qihang Wu; Daifeng Li; Lu Huang; Biyun Ye; | 2020-01-01 | |
1366 | Argumentative Relation Classification with Background Knowledge IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A common conception is that the understanding of relations that hold between argument units requires knowledge beyond the text. But to date, argument analysis systems that … |
DEBJIT PAUL et. al. | 2020-01-01 | |
1367 | Adaptive Graph Convolutional Networks with Attention Mechanism for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In the relationship extraction task of NLP, how to effective use of the rich structural information on the dependency tree is a challenging research problem. To better learn the … |
Zhixin Li; Yara Sun; Suqin Tang; Canlong Zhang; Huifang Ma; | 2020 International Joint Conference on Neural Networks … | 2020-01-01 |
1368 | Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples … |
BIN JI et. al. | 2020-01-01 | |
1369 | Dynamic Chunkwise CNN for Distantly Supervised Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Sentence representation learning is a key component in distantly supervised relation extraction. Text chunks (i.e., a group of n consecutive words) are meaningful units to … |
Fangbing Liu; Qing Wang; | 2020 IEEE International Conference on Big Data (Big Data) | 2020-01-01 |
1370 | Cross-Lingual Transfer for Hindi Discourse Relation Identification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Discourse relations between two textual spans in a document attempt to capture the coherent structure which emerges in language use. Automatic classification of these relations … |
Anirudh Dahiya; Manish Shrivastava; Dipti Misra Sharma; | 2020-01-01 | |
1371 | Threat Intelligence Relationship Extraction Based on Distant Supervision and Reinforcement Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recent years, threat intelligence has become a new hotspot in cybersecurity. It analyzes and predicts attacks that have occurred and have not occurred, and plays an important … |
Jie Yang; Qiuyun Wang; Changxin Su; Xuren Wang; | 2020-01-01 | |
1372 | Efficient Lifelong Relation Extraction with Dynamic Regularization Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction has received increasing attention due to its important role in natural language processing applications. However, most existing methods are designed for a … |
Hangjie Shen; Shenggen Ju; Jieping Sun; Run Chen; Yuezhong Liu; | 2020-01-01 | |
1373 | Focusing Visual Relation Detection on Relevant Relations with Prior Potentials Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Understanding images relies on the understanding of how visible objects are linked to each other. Current approaches of Visual Relation Detection (VRD) are hindered by the high … |
François Plesse; Alexandru-Lucian Gînsca; Bertrand Delezoide; Françoise J. Prêteux; | 2020 IEEE Winter Conference on Applications of Computer … | 2020-01-01 |
1374 | The Exploration of The Reasoning Capability of BERT in Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation classification task is to predict the relation between the entity pair in a given sentence. Most of these sentences have certain words or schema that can help to extract … |
Lili Li; Xin Xin; Ping Guo; | 2020 10th International Conference on Information Science … | 2020-01-01 |
1375 | Open Entity Semantic Relation Extraction in Big Data Environment Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Aiming at the problem that traditional entity relationship extraction needs to define a comprehensive entity relationship type system, poor portability, and the inability to … |
Li Zhen; Yang Zengchun; | 2020 International Conference on Cyber-Enabled Distributed … | 2020-01-01 |
1376 | Research on Open Entity Semantic Relation Extraction in Big Data Environment Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Aiming at the problem that traditional entity relationship extraction needs to define a comprehensive entity relationship type system, poor portability, and the inability to … |
Li Zhen; | 2020 International Conference on Cyber-Enabled Distributed … | 2020-01-01 |
1377 | Utilizing Gated Convolutional Picker to Enhance Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distant supervision is an useful way to automatically gather training data for relation extraction. Aiming at the question of label marking error in distant supervision process, … |
Qian Yi; Guixuan Zhang; Shuwu Zhang; | 2020 International Conference on Culture-oriented Science & … | 2020-01-01 |
1378 | Knowledge-guided Convolutional Networks For Chemical-Disease Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Results: This paper proposes a novel model called Knowledge-guided Convolutional Networks (KCN) to leverage prior knowledge for CDR extraction. |
HUIWEI ZHOU et. al. | arxiv-cs.CL | 2019-12-22 |
1379 | Combining Context And Knowledge Representations For Chemical-Disease Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. |
HUIWEI ZHOU et. al. | arxiv-cs.CL | 2019-12-22 |
1380 | The Performance Evaluation Of Multi-representation In The Deep Learning Models For Relation Extraction Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work addresses the question of how is improved the relation extraction using different types of representations generated by pretrained language representation models. |
Jefferson A. Peña Torres; Raul Ernesto Gutierrez; Victor A. Bucheli; Fabio A. Gonzalez O; | arxiv-cs.CL | 2019-12-17 |
1381 | Effective Attention Modeling For Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism. |
Tapas Nayak; Hwee Tou Ng; | arxiv-cs.CL | 2019-12-08 |
1382 | Enhancing Relation Extraction Using Syntactic Indicators And Sentential Contexts IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To leverage both syntactic indicators and sentential contexts, we propose an indicator-aware approach for relation extraction. |
Qiongxing Tao; Xiangfeng Luo; Hao Wang; | arxiv-cs.CL | 2019-12-04 |
1383 | Relation Extraction Via Attention-Based CNNs Using Token-Level Representations Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is an important task in the field of natural language processing (NLP). Most existing methods usually utilize word-level representations, ignoring massive … |
Yan Wang; Xin Xin; Ping Guo; | 2019 15th International Conference on Computational … | 2019-12-01 |
1384 | Are Noisy Sentences Useless For Distant Supervised Relation Extraction? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences. |
Yuming Shang; | arxiv-cs.CL | 2019-11-21 |
1385 | Improving Distant Supervised Relation Extraction By Dynamic Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to leverage this connection to improve the relation extraction accuracy. |
Yanjie Gou; Yinjie Lei; Lingqiao Liu; Pingping Zhang; Xi Peng; | arxiv-cs.CL | 2019-11-15 |
1386 | Leveraging Dependency Forest For Neural Medical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We investigate a method to alleviate this problem by utilizing dependency forests. |
LINFENG SONG et. al. | arxiv-cs.CL | 2019-11-11 |
1387 | Towards Understanding Gender Bias In Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We create WikiGenderBias, a distantly supervised dataset with a human annotated test set. |
ANDREW GAUT et. al. | arxiv-cs.LG | 2019-11-09 |
1388 | Relation Adversarial Network For Low Resource Knowledge Graph Completion IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available. |
NINGYU ZHANG et. al. | arxiv-cs.CL | 2019-11-08 |
1389 | On The Effectiveness Of The Pooling Methods For Biomedical Relation Extraction With Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to answer this question, in this work, we conduct a comprehensive study to evaluate the effectiveness of different pooling mechanisms for the deep learning models in biomedical RE. |
Tuan Ngo Nguyen; Franck Dernoncourt; Thien Huu Nguyen; | arxiv-cs.CL | 2019-11-04 |
1390 | A Fine-grained And Noise-aware Method For Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address these problems, we propose two reasonable assumptions and craft reinforcement learning to capture the expressive sentence for each relation mentioned in a bag. |
Jianfeng Qu; Wen Hua; Dantong Ouyang; Xiaofang Zhou; Ximing Li; | cikm | 2019-11-03 |
1391 | Nested Relation Extraction With Iterative Neural Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formally formulate the nested relation extraction problem, and come up with a solution using Iterative Neural Network. |
Yixuan Cao; Dian Chen; Hongwei Li; Ping Luo; | cikm | 2019-11-03 |
1392 | Large Margin Prototypical Network For Few-shot Relation Classification With Fine-grained Features IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider few-shot learning is of great practical significance to RC and thus improve a modern framework of metric learning for few-shot RC. |
Miao Fan; Yeqi Bai; Mingming Sun; Ping Li; | cikm | 2019-11-03 |
1393 | Enriching Pre-trained Language Model With Entity Information For Relation Classification IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task. |
Shanchan Wu; Yifan He; | cikm | 2019-11-03 |
1394 | Learning The Extraction Order Of Multiple Relational Facts In A Sentence With Reinforcement Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we argue that the extraction order is important in this task. |
XIANGRONG ZENG et. al. | emnlp | 2019-11-02 |
1395 | Entity, Relation, And Event Extraction With Contextualized Span Representations IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. |
David Wadden; Ulme Wennberg; Yi Luan; Hannaneh Hajishirzi; | emnlp | 2019-11-02 |
1396 | Cross-lingual Structure Transfer For Relation And Event Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate the suitability of cross-lingual structure transfer techniques for these tasks. |
ANANYA SUBBURATHINAM et. al. | emnlp | 2019-11-02 |
1397 | Looking Beyond Label Noise: Shifted Label Distribution Matters In Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study the problem what limits the performance of DS-trained neural models, conduct thorough analyses, and identify a factor that can influence the performance greatly, shifted label distribution. |
Qinyuan Ye; Liyuan Liu; Maosen Zhang; Xiang Ren; | emnlp | 2019-11-02 |
1398 | Next Sentence Prediction Helps Implicit Discourse Relation Classification Within And Across Domains IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We here show that this shortcoming can be effectively addressed by using the bidirectional encoder representation from transformers (BERT) proposed by Devlin et al. (2019), which were trained on a next-sentence prediction task, and thus encode a representation of likely next sentences. |
Wei Shi; Vera Demberg; | emnlp | 2019-11-02 |
1399 | Weakly Supervised Cross-lingual Semantic Relation Classification Via Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a cross-lingual relation classifier trained only with English examples and a bilingual dictionary. |
Yogarshi Vyas; Marine Carpuat; | emnlp | 2019-11-02 |
1400 | Nearly-Unsupervised Hashcode Representations For Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. |
Sahil Garg; Aram Galstyan; Greg Ver Steeg; Guillermo Cecchi; | emnlp | 2019-11-02 |
1401 | Open Relation Extraction: Relational Knowledge Transfer From Supervised Data To Unsupervised Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this issue, we propose Relational Siamese Networks (RSNs) to learn similarity metrics of relations from labeled data of pre-defined relations, and then transfer the relational knowledge to identify novel relations in unlabeled data. |
RUIDONG WU et. al. | emnlp | 2019-11-02 |
1402 | Self-Attention Enhanced CNNs And Collaborative Curriculum Learning For Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel model that employs a collaborative curriculum learning framework to reduce the effects of mislabelled data. |
Yuyun Huang; Jinhua Du; | emnlp | 2019-11-02 |
1403 | FewRel 2.0: Towards More Challenging Few-Shot Relation Classification IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? |
TIANYU GAO et. al. | emnlp | 2019-11-02 |
1404 | Connecting The Dots: Document-level Neural Relation Extraction With Edge-oriented Graphs IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We thus propose an edge-oriented graph neural model for document-level relation extraction. |
Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou; | emnlp | 2019-11-02 |
1405 | Easy First Relation Extraction With Information Redundancy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an easy first approach for relation extraction with information redundancies, embedded in the results produced by local sentence level extractors, during which conflict decisions are resolved with domain and uniqueness constraints. |
Shuai Ma; Gang Wang; Yansong Feng; Jinpeng Huai; | emnlp | 2019-11-02 |
1406 | Leveraging 2-hop Distant Supervision From Table Entity Pairs For Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a new strategy named 2-hop DS to enhance distantly supervised RE, based on the observation that there exist a large number of relational tables on the Web which contain entity pairs that share common relations. |
xiang deng; Huan Sun; | emnlp | 2019-11-02 |
1407 | Towards Extracting Medical Family History From Natural Language Interactions: A New Dataset And Baselines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new dataset consisting of natural language interactions annotated with medical family histories, obtained during interactions with a genetic counselor and through crowdsourcing, following a questionnaire created by experts in the domain. |
Mahmoud Azab; Stephane Dadian; Vivi Nastase; Larry An; Rada Mihalcea; | emnlp | 2019-11-02 |
1408 | Neural Cross-Lingual Relation Extraction Based On Bilingual Word Embedding Mapping IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new approach for cross-lingual RE model transfer based on bilingual word embedding mapping. |
Jian Ni; Radu Florian; | emnlp | 2019-11-02 |
1409 | Uncover The Ground-Truth Relations In Distant Supervision: A Neural Expectation-Maximization Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To cope with this challenge, we propose a novel label-denoising framework that combines neural network with probabilistic modelling, which naturally takes into account the noisy labels during learning. |
Junfan Chen; Richong Zhang; Yongyi Mao; Hongyu Guo; Jie Xu; | emnlp | 2019-11-02 |
1410 | Rewarding Coreference Resolvers For Being Consistent With World Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. |
RAHUL ARALIKATTE et. al. | emnlp | 2019-11-02 |
1411 | Learning To Infer Entities, Properties And Their Relations From Clinical Conversations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We extend the SAT model to jointly infer not only entities and their properties but also relations between them. |
Nan Du; Mingqiu Wang; Linh Tran; Gang Lee; Izhak Shafran; | emnlp | 2019-11-02 |
1412 | Cross-Sentence N-ary Relation Extraction Using Lower-Arity Universal Schemas IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach to cross-sentence n-ary relation extraction based on universal schemas. |
Kosuke Akimoto; Takuya Hiraoka; Kunihiko Sadamasa; Mathias Niepert; | emnlp | 2019-11-02 |
1413 | Improving Distantly-Supervised Relation Extraction With Joint Label Embedding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel multi-layer attention-based model to improve relation extraction with joint label embedding. |
LINMEI HU et. al. | emnlp | 2019-11-02 |
1414 | Stock Trend Extraction Using Rule-based and Syntactic Feature-based Relationships Between Named Entities Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Many research topics still debate to predict the trends of a stock in the financial markets. Trend extraction is an important part of the information retrieved from the financial … |
Ei Thwe Khaing; M. Thein; M. Lwin; | 2019 International Conference on Advanced Information … | 2019-11-01 |
1415 | BOUN-ISIK Participation: An Unsupervised Approach for The Named Entity Normalization and Relation Extraction of Bacteria Biotopes Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper presents our participation to the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria … |
Ilknur Karadeniz; Ömer Faruk Tuna; Arzucan Özgür; | EMNLP | 2019-11-01 |
1416 | A Multi-Task Learning Framework for Extracting Bacteria Biotope Information Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper presents a novel transfer multi-task learning method for Bacteria Biotope rel+ner task at BioNLP-OST 2019. To alleviate the data deficiency problem in domain-specific … |
Qi Zhang; Chao Liu; Ying Chi; Xuansong Xie; Xiansheng Hua; | EMNLP | 2019-11-01 |
1417 | Deep Bidirectional Transformers For Relation Extraction Without Supervision IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from a knowledge base. |
Yannis Papanikolaou; Ian Roberts; Andrea Pierleoni; | arxiv-cs.LG | 2019-11-01 |
1418 | Pose-Aware Multi-Level Feature Network for Human Object Interaction Detection IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address those challenges, we propose a multi-level relation detection strategy that utilizes human pose cues to capture global spatial configurations of relations and as an attention mechanism to dynamically zoom into relevant regions at human part level. |
Bo Wan; Desen Zhou; Yongfei Liu; Rongjie Li; Xuming He; | iccv | 2019-10-24 |
1419 | Semantic Graph Convolutional Network For Implicit Discourse Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel and effective Semantic Graph Convolutional Network (SGCN) to enhance the modeling of inter-argument semantics on a deeper interaction level for implicit discourse relation classification. |
YINGXUE ZHANG et. al. | arxiv-cs.CL | 2019-10-21 |
1420 | HiExpan: Task-Guided Taxonomy Construction By Hierarchical Tree Expansion IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus and allow users to input a seed taxonomy, serving as the task guidance. |
JIAMING SHEN et. al. | arxiv-cs.CL | 2019-10-17 |
1421 | Extraction of Chemical–protein Interactions from The Literature Using Neural Networks and Narrow Instance Representation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The scientific literature contains large amounts of information on genes, proteins, chemicals and their interactions. Extraction and integration of this information in curated … |
Rui Antunes; Sérgio Matos; | Database: The Journal of Biological Databases and Curation | 2019-10-17 |
1422 | Event Temporal Relation Classification Based on Graph Convolutional Networks Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qianwen Dai; F. Kong; Qianying Dai; | NLPCC | 2019-10-09 |
1423 | REET: Joint Relation Extraction and Entity Typing Via Multi-task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View |
HONGTAO LIU et. al. | NLPCC | 2019-10-09 |
1424 | Feature-Level Attention Based Sentence Encoding for Neural Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Longqi Dai; Bo Xu; Hui Song; | Natural Language Processing and Chinese Computing | 2019-10-09 |
1425 | Dynamic Label Correction for Distant Supervision Relation Extraction Via Semantic Similarity Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xinyu Zhu; Gongshen Liu; Bo Su; Jan P. Nees; | NLPCC | 2019-10-09 |
1426 | Learning High-order Structural And Attribute Information By Knowledge Graph Attention Networks For Enhancing Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome these limitations, this paper propose a novel knowledge graph embedding method, named KANE, which is inspired by the recent developments of graph convolutional networks (GCN). |
WENQIANG LIU et. al. | arxiv-cs.AI | 2019-10-09 |
1427 | Linguistically Informed Relation Extraction And Neural Architectures For Nested Named Entity Recognition In BioNLP-OST 2019 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents our findings from participating in BioNLP Shared Tasks 2019. |
Usama Yaseen; Pankaj Gupta; Hinrich Schütze; | arxiv-cs.CL | 2019-10-08 |
1428 | Improving Relation Extraction With Knowledge-attention IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel knowledge-attention encoder which incorporates prior knowledge from external lexical resources into deep neural networks for relation extraction task. |
Pengfei Li; Kezhi Mao; Xuefeng Yang; Qi Li; | arxiv-cs.CL | 2019-10-07 |
1429 | Type-aware Convolutional Neural Networks For Slot Filling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the relation classification component of a slot filling system. |
Heike Adel; Hinrich Schütze; | arxiv-cs.CL | 2019-10-01 |
1430 | OpenNRE: An Open And Extensible Toolkit For Neural Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). |
XU HAN et. al. | arxiv-cs.CL | 2019-09-28 |
1431 | Fine-tune Bert For DocRED With Two-step Process IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We argue that such simple baselines are not strong enough to model to complex interaction between entities. |
Hong Wang; Christfried Focke; Rob Sylvester; Nilesh Mishra; William Wang; | arxiv-cs.CL | 2019-09-26 |
1432 | Biomedical Relation Extraction With Pre-trained Language Representations And Minimal Task-specific Architecture IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents our participation in the AGAC Track from the 2019 BioNLP Open Shared Tasks. |
Ashok Thillaisundaram; Theodosia Togia; | arxiv-cs.CL | 2019-09-26 |
1433 | Deep Structured Neural Network For Event Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel deep structured learning framework for event temporal relation extraction. |
RUJUN HAN et. al. | arxiv-cs.CL | 2019-09-22 |
1434 | Dependency-based Text Graphs For Keyphrase And Summary Extraction With Applications To Interactive Content Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We build a bridge between neural network-based machine learning and graph-based natural language processing and introduce a unified approach to keyphrase, summary and relation extraction by aggregating dependency graphs from links provided by a deep-learning based dependency parser. |
Paul Tarau; Eduardo Blanco; | arxiv-cs.AI | 2019-09-20 |
1435 | Triplet-Aware Scene Graph Embeddings IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we train scene graph embeddings in a layout generation task with different forms of supervision, specifically introducing triplet super-vision and data augmentation. |
Brigit Schroeder; Subarna Tripathi; Hanlin Tang; | arxiv-cs.CV | 2019-09-19 |
1436 | Argumentative Relation Classification As Plausibility Ranking IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this aim, we propose a simple reconstruction trick which enables us to build minimal pairs of plausible and implausible texts by simulating natural contexts in which two argumentative units are likely or unlikely to appear. |
Juri Opitz; | arxiv-cs.CL | 2019-09-19 |
1437 | Span-based Joint Entity and Relation Extraction with Transformer Pre-training IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce SpERT, an attention model for span-based joint entity and relation extraction. |
Markus Eberts; Adrian Ulges; | arxiv-cs.CL | 2019-09-17 |
1438 | Taxonomical Hierarchy Of Canonicalized Relations From Multiple Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work addresses two important questions pertinent to Relation Extraction (RE). |
Akshay Parekh; Ashish Anand; Amit Awekar; | arxiv-cs.CL | 2019-09-13 |
1439 | Global Locality In Biomedical Relation And Event Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an approach to both relation and event extraction, for simultaneously predicting relationships between all mention pairs in a text. |
Elaheh ShafieiBavani; Antonio Jimeno Yepes; Xu Zhong; David Martinez Iraola; | arxiv-cs.CL | 2019-09-10 |
1440 | Nearly-Unsupervised Hashcode Representations For Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. |
Sahil Garg; Aram Galstyan; Greg Ver Steeg; Guillermo Cecchi; | arxiv-cs.LG | 2019-09-09 |
1441 | Rewarding Coreference Resolvers For Being Consistent With World Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. |
RAHUL ARALIKATTE et. al. | arxiv-cs.CL | 2019-09-05 |
1442 | Joint Event And Temporal Relation Extraction With Shared Representations And Structured Prediction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. |
Rujun Han; Qiang Ning; Nanyun Peng; | arxiv-cs.CL | 2019-09-02 |
1443 | Which Aspects of Discourse Relations Are Hard to Learn? Primitive Decomposition for Discourse Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Discourse relation classification has proven to be a hard task, with rather low performance on several corpora that notably differ on the relation set they use. We propose to … |
C. Roze; Chloé Braud; Philippe Muller; | SIGDIAL Conferences | 2019-09-01 |
1444 | Learning To Infer Entities, Properties And Their Relations From Clinical Conversations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). |
Nan Du; Mingqiu Wang; Linh Tran; Gang Li; Izhak Shafran; | arxiv-cs.CL | 2019-08-30 |
1445 | Temporal Reasoning Graph For Activity Recognition IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales. |
Jingran Zhang; Fumin Shen; Xing Xu; Heng Tao Shen; | arxiv-cs.CV | 2019-08-26 |
1446 | Transfer Learning For Relation Extraction Via Relation-Gated Adversarial Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve these problems, we propose a novel model of relation-gated adversarial learning for relation extraction, which extends the adversarial based domain adaptation. |
NINGYU ZHANG et. al. | arxiv-cs.LG | 2019-08-22 |
1447 | Fine-tuning BERT For Joint Entity And Relation Extraction In Chinese Medical Text IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a focused attention model for the joint entity and relation extraction task. |
KUI XUE et. al. | arxiv-cs.CL | 2019-08-21 |
1448 | Populating Web Scale Knowledge Graphs Using Distantly Supervised Relation Extraction And Validation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. |
Sarthak Dash; Michael R. Glass; Alfio Gliozzo; Mustafa Canim; | arxiv-cs.CL | 2019-08-21 |
1449 | Fine-grained Sentiment Analysis With Faithful Attention IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Surprisingly, we found that despite reasonable performance, the model’s attention was often systematically misaligned with the words that contribute to sentiment. |
Ruiqi Zhong; Steven Shao; Kathleen McKeown; | arxiv-cs.CL | 2019-08-19 |
1450 | Few-shot Text Classification With Distributional Signatures IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore meta-learning for few-shot text classification. |
Yujia Bao; Menghua Wu; Shiyu Chang; Regina Barzilay; | arxiv-cs.CL | 2019-08-16 |
1451 | BERT-Based Multi-Head Selection For Joint Entity-Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we report our method for the Information Extraction task in 2019 Language and Intelligence Challenge. Second, we introduce a large-scale Baidu Baike corpus for entity recognition pre-training, which is of weekly supervised learning since there is no actual named entity label. |
Weipeng Huang; Xingyi Cheng; Taifeng Wang; Wei Chu; | arxiv-cs.CL | 2019-08-16 |
1452 | X-WikiRE: A Large, Multilingual Resource For Relation Extraction As Machine Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. |
Mostafa Abdou; Cezar Sas; Rahul Aralikatte; Isabelle Augenstein; Anders Søgaard; | arxiv-cs.CL | 2019-08-14 |
1453 | Improving Cross-Domain Performance For Relation Extraction Via Dependency Prediction And Information Flow Control IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel method to use dependency trees in RE for deep learning models that jointly predicts dependency and semantics relations. |
Amir Pouran Ben Veyseh; Thien Nguyen; Dejing Dou; | ijcai | 2019-08-09 |
1454 | Relation Extraction Using Supervision From Topic Knowledge Of Relation Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we mine the topic knowledge of a relation to explicitly represent the semantics of this relation, and model relation extraction as a matching problem. |
HAIYUN JIANG et. al. | ijcai | 2019-08-09 |
1455 | Beyond Word Attention: Using Segment Attention In Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to incorporate such segment information into neural relation extractor. |
BOWEN YU et. al. | ijcai | 2019-08-09 |
1456 | Decoding Chinese User Generated Categories for Fine-Grained Knowledge Harvesting Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: User Generated Categories (UGCs) are short but informative phrases that reflect how people describe and organize entities. UGCs express semantic relations among entities … |
Chengyu Wang; Yan Fan; Xiaofeng He; Aoying Zhou; | IEEE Transactions on Knowledge and Data Engineering | 2019-08-01 |
1457 | Relation Extraction Via Domain-aware Transfer Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach called, Relation Extraction via Domain-aware Transfer Learning (ReTrans), to extract relation mentions from a given text corpus by exploring the experience from a large amount of existing KBs which may not be closely related to the target relation. |
Shimin Di; Yanyan Shen; Lei Chen; | kdd | 2019-08-01 |
1458 | Zero-shot Transfer For Implicit Discourse Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a new system using zero-shot transfer learning for implicit discourse relation classification, where the only resource used for the target language is unannotated parallel text. |
Murathan Kurfalı; Robert Östling; | arxiv-cs.CL | 2019-07-30 |
1459 | DocRED: A Large-Scale Document-Level Relation Extraction Dataset IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In order to acceleratethe research on document-level RE, we in-troduce DocRED, a new dataset constructedfrom Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE fromplain text; (2) DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document; (3) alongwith the human-annotated data, we also offer large-scale distantly supervised data, whichenables DocRED to be adopted for both supervised and weakly supervised scenarios. |
YUAN YAO et. al. | acl | 2019-07-28 |
1460 | Graph Neural Networks With Generated Parameters For Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel graph neural network with generated parameters (GP-GNNs). |
HAO ZHU et. al. | acl | 2019-07-28 |
1461 | Fine-tuning Pre-Trained Transformer Language Models To Distantly Supervised Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this gap, we utilize a pre-trained language model, the OpenAI Generative Pre-trained Transformer (GPT) (Radford et al., 2018). |
Christoph Alt; Marc Hübner; Leonhard Hennig,; | acl | 2019-07-28 |
1462 | Attention Guided Graph Convolutional Networks For Relation Extraction IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. |
Zhijiang Guo; Yan Zhang; Wei Lu,; | acl | 2019-07-28 |
1463 | Span-Level Model For Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address these concerns, we present a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
Kalpit Dixit; Yaser Al-Onaizan,; | acl | 2019-07-28 |
1464 | Entity-Relation Extraction As Multi-Turn Question Answering IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new paradigm for the task of entity-relation extraction. |
XIAOYA LI et. al. | acl | 2019-07-28 |
1465 | Learning Representation Mapping For Relation Detection In Knowledge Base Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple mapping method, named representation adapter, to learn the representation mapping for both seen and unseen relations based on previously learned relation embedding. |
PENG WU et. al. | acl | 2019-07-28 |
1466 | Matching The Blanks: Distributional Similarity For Relation Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we build on extensions of Harris’ distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. |
Livio Baldini Soares; Nicholas FitzGerald; Jeffrey Ling; Tom Kwiatkowski,; | acl | 2019-07-28 |
1467 | Fine-Grained Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. |
Siddharth Vashishtha; Benjamin Van Durme; Aaron Steven White,; | acl | 2019-07-28 |
1468 | Collocation Classification With Unsupervised Relation Vectors IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between which idiosyncratic lexical relations hold. |
Luis Espinosa Anke; Steven Schockaert; Leo Wanner,; | acl | 2019-07-28 |
1469 | GraphRel: Modeling Text As Relational Graphs For Joint Entity And Relation Extraction IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. |
Tsu-Jui Fu; Peng-Hsuan Li; Wei-Yun Ma,; | acl | 2019-07-28 |
1470 | Unsupervised Information Extraction: Regularizing Discriminative Approaches With Relation Distribution Losses IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome this limitation, we introduce a skewness loss which encourages the classifier to predict a relation with confidence given a sentence, and a distribution distance loss enforcing that all relations are predicted in average. |
Étienne Simon; Vincent Guigue; Benjamin Piwowarski,; | acl | 2019-07-28 |
1471 | Inter-sentence Relation Extraction With Document-level Graph Convolutional Neural Network IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. |
Sunil Kumar Sahu; Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou,; | acl | 2019-07-28 |
1472 | Neural Relation Extraction For Knowledge Base Enrichment IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This way, NED errors may cause extraction errors that affect the overall precision and recall.To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. |
Bayu Distiawan Trisedya; Gerhard Weikum; Jianzhong Qi; Rui Zhang,; | acl | 2019-07-28 |
1473 | Chinese Relation Extraction With Multi-Grained Information And External Linguistic Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction to take advantage of multi-grained language information and external linguistic knowledge. |
Ziran Li; Ning Ding; Zhiyuan Liu; Haitao Zheng; Ying Shen,; | acl | 2019-07-28 |
1474 | Exploiting Entity BIO Tag Embeddings And Multi-task Learning For Relation Extraction With Imbalanced Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate this problem, we propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
WEI YE et. al. | acl | 2019-07-28 |
1475 | ARNOR: Attention Regularization Based Noise Reduction For Distant Supervision Relation Classification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ARNOR, a novel Attention Regularization based NOise Reduction framework for distant supervision relation classification. |
Wei Jia; Dai Dai; Xinyan Xiao; Hua Wu,; | acl | 2019-07-28 |
1476 | Model-Agnostic Meta-Learning For Relation Classification With Limited Supervision IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we frame the task of supervised relation classification as an instance of meta-learning. |
Abiola Obamuyide; Andreas Vlachos,; | acl | 2019-07-28 |
1477 | Multi-Level Matching And Aggregation Network For Few-Shot Relation Classification IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. |
Zhi-Xiu Ye; Zhen-Hua Ling,; | acl | 2019-07-28 |
1478 | DIAG-NRE: A Neural Pattern Diagnosis Framework For Distantly Supervised Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To ease the labor-intensive workload of pattern writing and enable the quick generalization to new relation types, we propose a neural pattern diagnosis framework, DIAG-NRE, that can automatically summarize and refine high-quality relational patterns from noise data with human experts in the loop. |
SHUN ZHENG et. al. | acl | 2019-07-28 |
1479 | Joint Type Inference On Entities And Relations Via Graph Convolutional Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the joint type inference task, we propose a novel graph convolutional network (GCN) running on an entity-relation bipartite graph. |
CHANGZHI SUN et. al. | acl | 2019-07-28 |
1480 | Multi-view Multitask Learning for Knowledge Base Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View |
HONGZHI ZHANG et. al. | Knowl. Based Syst. | 2019-07-26 |
1481 | Hybrid Neural Tagging Model For Open Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to overcome these difficulties, we build a large-scale, high-quality training corpus in a fully automated way, and design a tagging scheme to assist in transforming the ORE task into a sequence tagging processing. |
Shengbin Jia; Yang Xiang; | arxiv-cs.CL | 2019-07-26 |
1482 | Answer-enhanced Path-aware Relation Detection Over Knowledge Base Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Knowledge-driven Relation Detection network (KRD) to interactively learn answer-enhanced question representations and path-aware relation representations for relation detection. |
Daoyuan Chen; Min Yang; Hai-Tao Zheng; Yaliang Li; Ying Shen; | sigir | 2019-07-20 |
1483 | Learning Representation Mapping For Relation Detection In Knowledge Base Question Answering IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a simple mapping method, named representation adapter, to learn the representation mapping for both seen and unseen relations based on previously learned relation embedding. |
PENG WU et. al. | arxiv-cs.CL | 2019-07-17 |
1484 | Improving Cross-Domain Performance For Relation Extraction Via Dependency Prediction And Information Flow Control IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel method to use dependency trees in RE for deep learning models that jointly predicts dependency and semantics relations. |
Amir Pouran Ben Veyseh; Thien Huu Nguyen; Dejing Dou; | arxiv-cs.CL | 2019-07-07 |
1485 | A Character-Enhanced Chinese Word Embedding Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distributed word representation has demonstrated its advantages in many natural language processing tasks. Such as named entity recognition, entity relation extraction, and text … |
Gang Yang; Hongzhe Xu; Tianhao He; Zaishang Cai; | 2019 International Joint Conference on Neural Networks … | 2019-07-01 |
1486 | Is Artificial Data Useful For Biomedical Natural Language Processing Algorithms? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a generic methodology to guide the generation of clinical text with key phrases. |
Zixu Wang; Julia Ive; Sumithra Velupillai; Lucia Specia; | arxiv-cs.CL | 2019-07-01 |
1487 | Exploiting Entity BIO Tag Embeddings And Multi-task Learning For Relation Extraction With Imbalanced Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate this problem, we propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
WEI YE et. al. | arxiv-cs.CL | 2019-06-20 |
1488 | Fine-tuning Pre-Trained Transformer Language Models To Distantly Supervised Relation Extraction IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this gap, we utilize a pre-trained language model, the OpenAI Generative Pre-trained Transformer (GPT) [Radford et al., 2018]. |
Christoph Alt; Marc Hübner; Leonhard Hennig; | arxiv-cs.CL | 2019-06-19 |
1489 | REflex: Flexible Framework For Relation Extraction In Multiple Domains IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we build a unifying framework for RE, applying this on three highly used datasets (from the general, biomedical and clinical domains) with the ability to be extendable to new datasets. |
Geeticka Chauhan; Matthew B. A. McDermott; Peter Szolovits; | arxiv-cs.CL | 2019-06-19 |
1490 | Attention Guided Graph Convolutional Networks For Relation Extraction IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. |
Zhijiang Guo; Yan Zhang; Wei Lu; | arxiv-cs.CL | 2019-06-18 |
1491 | Transfer Learning For Causal Sentence Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We consider the task of detecting sentences that express causality, as a step towards mining causal relations from texts. |
Manolis Kyriakakis; Ion Androutsopoulos; Joan Ginés i Ametllé; Artur Saudabayev; | arxiv-cs.CL | 2019-06-18 |
1492 | BERE: An Accurate Distantly Supervised Biomedical Entity Relation Extraction Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose BERE, a deep learning based model which uses Gumbel Tree-GRU to learn sentence structures and joint embedding to incorporate entity information. Because the existing dataset are relatively small, we further construct a much larger drug-target interaction extraction (DTIE) dataset by distant supervision. |
Lixiang Hong; JinJian Lin; Jiang Tao; Jianyang Zeng; | arxiv-cs.CL | 2019-06-17 |
1493 | Multi-Level Matching And Aggregation Network For Few-Shot Relation Classification IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. |
Zhi-Xiu Ye; Zhen-Hua Ling; | arxiv-cs.CL | 2019-06-16 |
1494 | Spatial-Aware Graph Relation Network for Large-Scale Object Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a Spatial-aware Graph Relation Network (SGRN) to adaptive discover and incorporate key semantic and spatial relationships for reasoning over each object. |
Hang Xu; Chenhan Jiang; Xiaodan Liang; Zhenguo Li; | cvpr | 2019-06-14 |
1495 | DocRED: A Large-Scale Document-Level Relation Extraction Dataset IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In order to verify the challenges of document-level RE, we implement recent state-of-the-art methods for RE and conduct a thorough evaluation of these methods on DocRED. |
YUAN YAO et. al. | arxiv-cs.CL | 2019-06-14 |
1496 | Antonym-Synonym Classification Based On New Sub-space Embeddings IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel approach entirely based on pre-trained embeddings. |
Muhammad Asif Ali; Yifang Sun; Xiaoling Zhou; Wei Wang; Xiang Zhao; | arxiv-cs.CL | 2019-06-13 |
1497 | A Structured Learning Approach To Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. |
Qiang Ning; Zhili Feng; Dan Roth; | arxiv-cs.CL | 2019-06-12 |
1498 | Inter-sentence Relation Extraction With Document-level Graph Convolutional Neural Network IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. |
Sunil Kumar Sahu; Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou; | arxiv-cs.CL | 2019-06-11 |
1499 | Improving Relation Extraction By Pre-trained Language Representations IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address these limitations, we introduce TRE, a Transformer for Relation Extraction, extending the OpenAI Generative Pre-trained Transformer [Radford et al., 2018]. |
Christoph Alt; Marc Hübner; Leonhard Hennig; | arxiv-cs.CL | 2019-06-07 |
1500 | Matching The Blanks: Distributional Similarity For Relation Learning IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we build on extensions of Harris’ distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. |
Livio Baldini Soares; Nicholas FitzGerald; Jeffrey Ling; Tom Kwiatkowski; | arxiv-cs.CL | 2019-06-07 |