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. Based in New York, Paper Digest is dedicated to producing high-quality text analysis results that people can acturally use on a daily basis. In the past 4 years, we have been serving users across the world with a number of exclusive services on ranking, search, tracking and review. This month we feature Literature Review Generator, which automatically generates literature review around any topic.
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TABLE 1: Paper Digest: Recent Papers on Relation Extraction
| Paper | Author(s) | Source | Date | |
|---|---|---|---|---|
| 1 | Temporal Relation Extraction with A Graph-Based Deep Biaffine Attention Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 2 | Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 3 | DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new open-source and extensible knowledge extraction toolkit, called DeepKE (Deep learning based Knowledge Extraction), supporting standard fully supervised, low-resource few-shot and document-level scenarios. |
NINGYU ZHANG et. al. | arxiv-cs.CL | 2022-01-10 |
| 4 | Mining Adverse Drug Reactions from Unstructured Mediums at Scale Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The complete solution is implemented as a unified NLP pipeline in a production-grade library built on top of Apache Spark, making it natively scalable and able to process millions of batch or streaming records on commodity clusters. |
Hasham Ul Haq; Veysel Kocaman; David Talby; | arxiv-cs.CL | 2022-01-04 |
| 5 | Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we instead focus on better utilizing the \textit{implicit knowledge} stored in pre-trained language models. |
Zi-Yi Dou; Nanyun Peng; | arxiv-cs.CL | 2022-01-01 |
| 6 | Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 7 | Deeper Clinical Document Understanding Using Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 8 | Unified Named Entity Recognition As Word-Word Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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. | arxiv-cs.CL | 2021-12-19 |
| 9 | Neural Architectures for Biological Inter-Sentence Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 10 | An Empirical Study on Relation Extraction in The Biomedical Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Most existing models are defined for relation extraction in the general domain. |
Yongkang Li; | arxiv-cs.CL | 2021-12-10 |
| 11 | 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 Orgs Related Experts Details 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 |
| 12 | Prompt-based Zero-shot Relation Classification with Semantic Knowledge Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 13 | From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 14 | Context-Dependent Semantic Parsing for Temporal Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 15 | Text Mining Drug/Chemical-Protein Interactions Using An Ensemble of BERT and T5 Based Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 16 | Predicting Document Coverage for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 17 | Multi-Attribute Relation Extraction (MARE) — Simplifying The Application of Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 18 | Learning Logic Rules for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 19 | Exploring Task Difficulty for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 20 | Entity Relation Extraction As Dependency Parsing in Visually Rich Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 21 | Distilling Relation Embeddings from Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 22 | Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 23 | ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations. |
Rujun Han; Xiang Ren; Nanyun Peng; | emnlp | 2021-11-05 |
| 24 | Document-level Entity-based Extraction As Template Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a generative framework for two document-level EE tasks: role-filler entity extraction (REE) and relation extraction (RE). |
Kung-Hsiang Huang; Sam Tang; Nanyun Peng; | emnlp | 2021-11-05 |
| 25 | Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 26 | Towards Realistic Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 27 | Relation Extraction with Word Graphs from N-grams Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 28 | Large-Scale Relation Learning for Question Answering Over Knowledge Bases with Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 29 | A Relation-Oriented Clustering Method for Open Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 30 | On The Relation Between Syntactic Divergence and Zero-Shot Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 31 | Unsupervised Relation Extraction: A Variational Autoencoder Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 32 | A Partition Filter Network for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 33 | Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 34 | Incorporating Medical Knowledge in BERT for Clinical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 35 | MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 36 | Graph Based Network with Contextualized Representations of Turns in Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 37 | Zero-Shot Information Extraction As A Unified Text-to-Triple Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We cast a suite of information extraction tasks into a text-to-triple translation framework. |
CHENGUANG WANG et. al. | emnlp | 2021-11-05 |
| 38 | Modular Self-Supervision for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 39 | Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 40 | Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 41 | Distantly Supervised Relation Extraction Using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 42 | Extracting Event Temporal Relations Via Hyperbolic Geometry IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 43 | Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 44 | CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in The Wild Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 45 | Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 46 | Separating Retention from Extraction in The Evaluation of End-to-end Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 47 | SERC: Syntactic and Semantic Sequence Based Event Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 48 | Diagnosing Errors in Video Relation Detectors Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 49 | Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 50 | Entity Relation Extraction As Dependency Parsing in Visually Rich Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 51 | A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 52 | A Simple, Strong and Robust Baseline for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore this hypothesis and come up with a novel aggregation scheme which we call Passage-Att. |
Vipul Rathore; Kartikeya Badola; Mausam; Parag Singla; | arxiv-cs.CL | 2021-10-14 |
| 53 | Social Fabric: Tubelet Compositions for Video Relation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 54 | FoodChem: A Food-chemical Relation Extraction Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 55 | MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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; Mausam; | arxiv-cs.CL | 2021-09-30 |
| 56 | SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 relation extraction. |
Yuxin Xiao; Zecheng Zhang; Yuning Mao; Carl Yang; Jiawei Han; | arxiv-cs.CL | 2021-09-24 |
| 57 | Separating Retention from Extraction in The Evaluation of End-to-end Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 58 | DisCoDisCo at The DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 59 | Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 60 | Slot Filling for Biomedical Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 to deal with zero-shot settings. |
Yannis Papanikolaou; Francine Bennett; | arxiv-cs.CL | 2021-09-17 |
| 61 | Pack Together: Entity and Relation Extraction with Levitated Marker Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel span representation approach, named Packed Levitated Markers, to consider the dependencies between the spans (pairs) by strategically packing the markers in the encoder. |
Deming Ye; Yankai Lin; Maosong Sun; | arxiv-cs.CL | 2021-09-13 |
| 62 | Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 63 | D-REX: Dialogue Relation Extraction with Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 64 | How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We demonstrate that the latter scenario provides positive effects on machine performance on two separate datasets. |
Hoang Van; Zheng Tang; Mihai Surdeanu; | arxiv-cs.CL | 2021-09-09 |
| 65 | Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 66 | Text-to-Table: A New Way of Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formalize text-to-table as a sequence-to-sequence (seq2seq) problem. |
Xueqing Wu; Jiacheng Zhang; Hang Li; | arxiv-cs.CL | 2021-09-06 |
| 67 | Semi-Automated Labeling of Requirement Datasets for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 68 | Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 69 | Does Knowledge Help General NLU? An Empirical Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we empirically investigated the contribution of external knowledge by measuring the end-to-end performance of language models with various knowledge integration methods. |
Ruochen Xu; Yuwei Fang; Chenguang Zhu; Michael Zeng; | arxiv-cs.CL | 2021-09-01 |
| 70 | Extracting All Aspect-polarity Pairs Jointly in A Text with Relation Extraction Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 71 | TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes TREND, a multi-tasking BERT-based model which learns to identify triggers for improving relation extraction. |
Po-Wei Lin; Shang-Yu Su; Yun-Nung Chen; | arxiv-cs.CL | 2021-08-31 |
| 72 | NEREL: A Russian Dataset with Nested Named Entities, Relations and Events Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. |
NATALIA LOUKACHEVITCH et. al. | arxiv-cs.CL | 2021-08-30 |
| 73 | Relation Extraction from Tables Using Artificially Generated Metadata Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 74 | Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 75 | A Hierarchical Entity Graph Convolutional Network for Relation Extraction Across Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 76 | SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 77 | Extracting Radiological Findings With Normalized Anatomical Information Using A Span-Based BERT Relation Extraction Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 78 | Video Relation Detection Via Tracklet Based Visual Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 79 | RTE: A Tool for Annotating Relation Triplets from Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 80 | MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in The Mobility Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 81 | An Effective System for Multi-format Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 82 | Consistent Inference for Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 83 | Knowledge-Enhanced Domain Adaptation in Few-Shot Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 84 | Cross-Network Learning with Partially Aligned Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, I propose partially aligned graph convolutional networks to learn node representations across the models. |
Meng Jiang; | kdd | 2021-08-12 |
| 85 | Time-aware Path Reasoning on Knowledge Graph for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations. |
YUYUE ZHAO et. al. | arxiv-cs.IR | 2021-08-05 |
| 86 | Multi-Scale Feature and Metric Learning for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 87 | Element Intervention for Open Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 88 | Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 89 | Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 90 | How Knowledge Graph and Attention Help? A Qualitative Analysis Into Bag-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 91 | Entity Enhancement for Implicit Discourse Relation Classification in The Biomedical Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 92 | Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 93 | SENT: Sentence-level Distant Relation Extraction Via Negative Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction. |
RUOTIAN MA et. al. | acl | 2021-07-26 |
| 94 | Revisiting The Negative Data of Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 95 | Entity Concept-enhanced Few-shot Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 96 | Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 97 | A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 98 | Verb Metaphor Detection Via Contextual Relation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 99 | How Knowledge Graph and Attention Help? A Quantitative Analysis Into Bag-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 100 | TIMERS: Document-level Temporal Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 101 | UniRE: A Unified Label Space for Entity Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 102 | CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 103 | ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models Via Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 104 | From Discourse to Narrative: Knowledge Projection for Event Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 105 | Improving Sentence-Level Relation Extraction Through Curriculum Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 106 | Clinical Relation Extraction Using Transformer-based Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 107 | What and When to Look?: Temporal Span Proposal Network for Video Visual Relation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To date, two representative methods have been proposed to tackle Video Visual Relation Detection (VidVRD): segment-based and window-based. |
Sangmin Woo; Junhyug Noh; Kangil Kim; | arxiv-cs.CV | 2021-07-15 |
| 108 | ReadsRE: Retrieval-Augmented Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 109 | Position Enhanced Mention Graph Attention Network for Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 110 | Benchmarking for Biomedical Natural Language Processing Tasks with A Domain Specific ALBERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present BioALBERT, a domain-specific adaptation of A Lite Bidirectional Encoder Representations from Transformers (ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical (MIMIC-III) corpora and fine tuned for 6 different tasks across 20 benchmark datasets. |
Usman Naseem; Adam G. Dunn; Matloob Khushi; Jinman Kim; | arxiv-cs.CL | 2021-07-09 |
| 111 | Injecting Knowledge Base Information Into End-to-End Joint Entity and Relation Extraction and Coreference Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 112 | Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we introduced a novel framework that enables the model to learn multi-omnics biological information about entities (proteins) with the help of additional multi-modal cues like molecular structure. |
Sriram Pingali; Shweta Yadav; Pratik Dutta; Sriparna Saha; | arxiv-cs.CL | 2021-07-01 |
| 113 | HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 114 | A Simple and Efficient Probabilistic Language Model for Code-Mixed Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 115 | Benchmarking Knowledge-driven Zero-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed six resources covering three tasks, i.e., zero-shot image classification (ZS-IMGC), zero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC). |
YUXIA GENG et. al. | arxiv-cs.AI | 2021-06-28 |
| 116 | Effective Cascade Dual-Decoder Model for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 117 | RECON: Relation Extraction Using Knowledge Graph Context in A Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 118 | A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 119 | Enquire One’s Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Hierarchy Expansion Framework (HEF), which fully exploits the hierarchical structure’s properties to maximize the coherence of expanded taxonomy. |
Suyuchen Wang; Ruihui Zhao; Xi Chen; Yefeng Zheng; Bang Liu; | www | 2021-06-25 |
| 120 | SENT: Sentence-level Distant Relation Extraction Via Negative Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 121 | Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 122 | Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 123 | Structured Sparse R-CNN for Direct Scene Graph Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead, from a perspective on SGG as a direct set prediction, this paper presents a simple, sparse, and unified framework for relation detection, termed as Structured Sparse R-CNN. |
Yao Teng; Limin Wang; | arxiv-cs.CV | 2021-06-20 |
| 124 | Graph-based Joint Pandemic Concern and Relation Extraction on Twitter Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 125 | Eider: Evidence-enhanced Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To make better use of the evidence sentences, in this paper, we propose a three-stage evidence-enhanced DocRE framework consisting of joint relation and evidence extraction, evidence-centered relation extraction (RE), and fusion of extraction results. |
Yiqing Xie; Jiaming Shen; Sha Li; Yuning Mao; Jiawei Han; | arxiv-cs.CL | 2021-06-16 |
| 126 | Document-level Relation Extraction As Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Herein, we propose a Document U-shaped Network for document-level relation extraction. |
NINGYU ZHANG et. al. | arxiv-cs.CL | 2021-06-07 |
| 127 | Let’s Be Explicit About That: Distant Supervision for Implicit Discourse Relation Classification Via Connective Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 128 | SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 129 | End-to-End Hierarchical Relation Extraction for Generic Form Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 130 | Discriminative Reasoning for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 131 | KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 132 | DiaKG: An Annotated Diabetes Dataset for Medical Knowledge Graph Construction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to accelerate the research for domain-specific knowledge graphs in the medical domain, we introduce DiaKG, a high-quality Chinese dataset for Diabetes knowledge graph, which contains 22,050 entities and 6,890 relations in total. |
DEJIE CHANG et. al. | arxiv-cs.CL | 2021-05-31 |
| 133 | PTR: Prompt Tuning with Rules for Text Classification IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 134 | Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce constraint graphs to model the dependencies between relation labels. |
Tianming Liang; Yang Liu; Xiaoyan Liu; Gaurav Sharma; Maozu Guo; | arxiv-cs.CL | 2021-05-24 |
| 135 | Integrating Lexical Information Into Entity Neighbourhood Representations for Relation Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 136 | Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 137 | A Frustratingly Easy Approach for Entity and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 138 | Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 139 | ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 140 | Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we define a new task in the NLP domain, incremental few-shot text classification, where the system incrementally handles multiple rounds of new classes. |
Congying Xia; Wenpeng Yin; Yihao Feng; Philip Yu; | naacl | 2021-05-23 |
| 141 | Open Hierarchical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 142 | Global Context for Improving Recognition of Online Handwritten Mathematical Expressions Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 143 | Boosting Span-based Joint Entity and Relation Extraction Via Squence Tagging Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 144 | Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 145 | KLUE: Korean Language Understanding Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Korean Language Understanding Evaluation (KLUE) benchmark. |
SUNGJOON PARK et. al. | arxiv-cs.CL | 2021-05-20 |
| 146 | Do Models Learn The Directionality of Relations? A New Evaluation: Relation Direction Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 147 | Relation Classification with Entity Type Restriction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 148 | Distantly Supervised Relation Extraction Via Recursive Hierarchy-Interactive Attention and Entity-Order Perception Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design a novel Recursive Hierarchy-Interactive Attention network (RHIA), which uses the hierarchical structure of the relation to model the interactive information between the relation levels to further handle long-tail relations. |
RIDONG HAN et. al. | arxiv-cs.CL | 2021-05-17 |
| 149 | Multi-Granularity Heterogeneous Graph for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 150 | Multi-Entity Collaborative Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 151 | More: A Metric Learning Based Framework for Open-Domain Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 152 | GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 153 | Discourse Relation Embeddings: Representing The Relations Between Discourse Segments in Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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; H Andrew Schwartz; | arxiv-cs.CL | 2021-05-04 |
| 154 | Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 155 | Multi-view Inference for Relation Extraction with Uncertain Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 156 | Explore BiLSTM-CRF-Based Models for Open Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 157 | Enriched Attention for Robust Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address this problem with an enriched attention mechanism. |
Heike Adel; Jannik Strötgen; | arxiv-cs.CL | 2021-04-22 |
| 158 | Extracting Adverse Drug Events from Clinical Notes Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 159 | Improving Biomedical Pretrained Language Models with Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 160 | Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the examination of available temporal relation annotations and human-like cognitive procedures, we propose a new Temporal Graph Transformer network to (1) explicitly find the connection between two events from a syntactic graph constructed from one or two continuous sentences, and (2) automatically locate the most indicative temporal cues from the path of the two event mentions as well as their surrounding concepts in the syntactic graph with a new temporal-oriented attention mechanism. |
Shuaicheng Zhang; Lifu Huang; Qiang Ning; | arxiv-cs.CL | 2021-04-19 |
| 161 | DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response, we propose a new dataset, DiS-ReX, which alleviates these issues. |
Abhyuday Bhartiya; Kartikeya Badola; Mausam; | arxiv-cs.CL | 2021-04-17 |
| 162 | Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 163 | Re-TACRED: Addressing Shortcomings of The TACRED Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 164 | KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we focus on incorporating knowledge 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 |
| 165 | A Sample-Based Training Method for Distantly Supervised Relation Extraction with Pre-Trained Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 166 | UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements As Multi-Turn Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 167 | A Question-answering Based Framework for Relation Extraction Validation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 168 | Deep Neural Networks for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 169 | Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 170 | Normal Vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 171 | Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 172 | Multi-facet Universal Schema Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 173 | Prototypical Representation Learning for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 174 | Structural Block Driven – Enhanced Convolutional Neural Representation for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 175 | Leveraging Unlabeled Data for Entity-Relation Extraction Through Probabilistic Constraint Satisfaction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 176 | Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 177 | Mention-centered Graph Neural Network for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 178 | Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 179 | A Review on Semi-Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 180 | Techniques for Jointly Extracting Entities and Relations: A Survey Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 181 | Dual Pointer Network for Fast Extraction of Multiple Relations in A Sentence Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 182 | REMOD: Relation Extraction for Modeling Online Discourse Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 183 | Better Call The Plumber: Orchestrating Dynamic Information Extraction Pipelines Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 184 | Contextual Argument Component Classification for Class Discussions Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 185 | Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 186 | WebRED: Effective Pretraining And Finetuning For Relation Extraction On The Web Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 187 | Decoding EEG Brain Activity for Multi-Modal Natural Language Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. |
NORA HOLLENSTEIN et. al. | arxiv-cs.CL | 2021-02-17 |
| 188 | Two Training Strategies for Improving Relation Extraction Over Universal Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 189 | An End-to-end Model for Entity-level Relation Extraction Using Multi-instance Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a joint model for entity-level relation extraction from documents. |
Markus Eberts; Adrian Ulges; | arxiv-cs.CL | 2021-02-11 |
| 190 | Curriculum-Meta Learning for Order-Robust Continual Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 191 | FL-MSRE: A Few-Shot Learning Based Approach to Multimodal Social Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 192 | Progressive Multi-task Learning with Controlled Information Flow for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 193 | Bootstrapping Relation Extractors Using Syntactic Search By Examples Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 194 | Making The Relation Matters: Relation of Relation Learning Network for Sentence Semantic Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 195 | KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 196 | Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 197 | Visual Relation Detection Using Hybrid Analogical Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 198 | Re-TACRED: Addressing Shortcomings of The TACRED Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 199 | DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 200 | GDPNet: Refining Latent Multi-View Graph for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 201 | Document-Level Relation Extraction with Reconstruction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 202 | Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 203 | An Improved Baseline for Sentence-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 204 | Neural Data Augmentation Via Example Extrapolation IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a data augmentation approach that performs neural Example Extrapolation (Ex2). |
Kenton Lee; Kelvin Guu; Luheng He; Tim Dozat; Hyung Won Chung; | arxiv-cs.CL | 2021-02-02 |
| 205 | Memorization Vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We identify leakage of training data into test data on several publicly available datasets used to evaluate NLP tasks, including named entity recognition and relation extraction, and study them to assess the impact of that leakage on the model’s ability to memorize versus generalize. |
Aparna Elangovan; Jiayuan He; Karin Verspoor; | arxiv-cs.CL | 2021-02-02 |
| 206 | Improving Distantly-Supervised Relation Extraction Through BERT-based Label & Instance Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 207 | Is Depression Related to Cannabis?: A Knowledge-infused Model for Entity and Relation Extraction with Limited Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 208 | LOME: Large Ontology Multilingual Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present LOME, a system for performing multilingual information extraction. |
PATRICK XIA et. al. | arxiv-cs.CL | 2021-01-28 |
| 209 | Enquire One’s Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Hierarchy Expansion Framework (HEF), which fully exploits the hierarchical structure’s properties to maximize the coherence of expanded taxonomy. |
Suyuchen Wang; Ruihui Zhao; Xi Chen; Yefeng Zheng; Bang Liu; | arxiv-cs.CL | 2021-01-27 |
| 210 | Laughing at You Or with You: The Role of Sarcasm in Shaping The Disagreement Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 211 | Process-Level Representation of Scientific Protocols with Interactive Annotation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 212 | Video Relation Detection with Trajectory-aware Multi-modal Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 213 | HIVE-4-MAT: Advancing The Ontology Infrastructure for Materials Science Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 214 | A Survey on Extraction of Causal Relations from Natural Language Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 215 | Structured Prediction As Translation Between Augmented Natural Languages IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 216 | EventPlus: A Temporal Event Understanding Pipeline Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 217 | Predicting Relative Depth Between Objects from Semantic Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper the extent to which semantic features can predict course relative depth is investigated. |
Stefan Cassar; Adrian Muscat; Dylan Seychell; | arxiv-cs.CV | 2021-01-12 |
| 218 | BERT-GT: Cross-sentence N-ary Relation Extraction with BERT and Graph Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 219 | Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 220 | Learning Better Sentence Representation with Syntax Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 221 | Deep Neural Network Based Relation Extraction: An Overview Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 222 | Curriculum-Meta Learning for Order-Robust Continual Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 223 | Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 224 | Pattern-based Bootstrapping Framework for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
S. S. Deepika; T. V. Geetha; | Eng. Appl. Artif. Intell. | 2021-01-01 |
| 225 | Joint Entity and Relation Extraction Model Based on Rich Semantics IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details |
Zhiqiang Geng; Yanhui Zhang; Yongming Han; | Neurocomputing | 2021-01-01 |
| 226 | 3-D Relation Network for Visual Relation Recognition in Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Video visual relation recognition aims at mining the dynamic relation instances between objects in the form of 〈 subject , predicate , object 〉 , such as … |
Qianwen Cao; Heyan Huang; Xindi Shang; Boran Wang; Tat-Seng Chua; | Neurocomputing | 2021-01-01 |
| 227 | Document-level Relation Extraction with Entity-Selection Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Changsen Yuan; Heyan Huang; Chong Feng; Ge Shi; Xiaochi Wei; | Inf. Sci. | 2021-01-01 |
| 228 | Improving Distantly-Supervised Relation Extraction Through BERT-Based Label and Instance Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 229 | A Joint Model for Entity and Relation Extraction Based on BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 230 | Relation Extraction with Type-aware Map Memories of Word Dependencies Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 231 | CovidBERT-Biomedical Relation Extraction for Covid-19 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 232 | Representation Iterative Fusion Based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Kang Zhao; Hua Xu; Yue Cheng; Xiaoteng Li; Kai Gao; | Knowl. Based Syst. | 2021-01-01 |
| 233 | ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 234 | Domain-specific Meta-embedding with Latent Semantic Structures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Meta-embedding aims at assembling pre-trained embeddings from various sources and producing more expressively powerful word representations. Many natural language … |
QIAN LIU et. al. | Inf. Sci. | 2021-01-01 |
| 235 | Memorization Vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). However, the presence … |
Aparna Elangovan; Jiayuan He; Karin Verspoor; | 2021-01-01 | |
| 236 | Discourse-level Relation Extraction Via Graph Pooling Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 237 | An Embarrassingly Simple Model For Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we model Dialogue RE as a multi-label classification task and propose a simple yet effective model named SimpleRE. |
Fuzhao Xue; Aixin Sun; Hao Zhang; Eng Siong Chng; | arxiv-cs.CL | 2020-12-27 |
| 238 | Towards Incorporating Entity-specific Knowledge Graph Information In Predicting Drug-Drug Interactions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore how to incorporate domain knowledge of the biomedical entities (such as drug, disease, genes), obtained from Knowledge Graph (KG) Embeddings, for predicting Drug-Drug Interaction from textual corpus. |
Ishani Mondal; | arxiv-cs.CL | 2020-12-21 |
| 239 | Regularized Attentive Capsule Network For Overlapped Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 240 | An Empirical Study of Using Pre-trained BERT Models for Vietnamese Relation Extraction Task at VLSP 2020 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 241 | R$^2$-Net: Relation Of Relation Learning Network For Sentence Semantic Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 242 | InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources For Distantly-supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 243 | Complex Relation Extraction: Challenges And Opportunities Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 244 | Improving Relation Extraction By Leveraging Knowledge Graph Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 245 | From Bag Of Sentences To Document: Distantly Supervised Relation Extraction Via Machine Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 246 | H-FND: Hierarchical False-Negative Denoising For Distant Supervision Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 247 | Event Guided Denoising For Multilingual Relation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 248 | Coarse-to-Fine Entity Representations for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 249 | Financial Document Causality Detection Shared Task (FinCausal 2020) IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 250 | Rel3D: A Minimally Contrastive Benchmark For Grounding Spatial Relations In 3D Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 251 | Automatic Extraction Of Ranked SNP-Phenotype Associations From Literature Through Detecting Neural Candidates, Negation And Modality Markers Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 252 | Learning Relation Prototype From Unlabeled Texts For Long-tail Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 253 | Joint Extraction Of Entity And Relation With Information Redundancy Elimination Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 254 | Experiments On Transfer Learning Architectures For Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 255 | RTFN: A Robust Temporal Feature Network For Time Series Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 256 | Dual Supervision Framework For Relation Extraction With Distant Supervision And Human Annotation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 257 | Learning Informative Representations Of Biomedical Relations With Latent Variable Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 258 | Entity Recognition And Relation Extraction From Scientific And Technical Texts In Russian Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 259 | Relation Extraction With Contextualized Relation Embedding (CRE) Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 260 | Zero-shot Relation Classification from Side Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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; | arxiv-cs.CL | 2020-11-13 |
| 261 | Pre-training Entity Relation Encoder With Intra-span And Inter-span Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 262 | Exposing Shallow Heuristics Of Relation Extraction Models With Challenge Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 263 | Let’s Stop Incorrect Comparisons In End-to-end Relation Extraction! Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 264 | Double Graph Based Reasoning For Document-level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 265 | FedED: Federated Learning Via Ensemble Distillation For Medical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 266 | Global-to-Local Neural Networks For Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 267 | SelfORE: Self-supervised Relational Feature Learning For Open Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 268 | APE: Argument Pair Extraction From Peer Review And Rebuttal Via Multi-task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 269 | Denoising Relation Extraction From Document-level Distant Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 270 | Two Are Better Than One: Joint Entity And Relation Extraction With Table-Sequence Encoders IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 271 | AutoPrompt: Eliciting Knowledge From Language Models With Automatically Generated Prompts IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 272 | Within-Between Lexical Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 273 | Biomedical Named Entity Recognition At Scale Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Reimplementing a Bi-LSTM-CNN-Char deep learning architecture on top of Apache Spark, we present a single trainable NER model that obtains new state-of-the-art results on seven public biomedical benchmarks without using heavy contextual embeddings like BERT. |
Veysel Kocaman; David Talby; | arxiv-cs.CL | 2020-11-12 |
| 274 | Recurrent Interaction Network For Jointly Extracting Entities And Classifying Relations Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 275 | An Empirical Study Of Pre-trained Transformers For Arabic Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 276 | Domain Knowledge Empowered Structured Neural Net For End-to-End Event Temporal Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 277 | Biomedical Information Extraction For Disease Gene Prioritization Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 278 | Minimize Exposure Bias Of Seq2Seq Models In Joint Entity And Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 279 | Active Testing: An Unbiased Evaluation Method For Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 280 | Unsupervised Relation Extraction From Language Models Using Constrained Cloze Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 281 | The RELX Dataset And Matching The Multilingual Blanks For Cross-lingual Relation Classification IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 282 | The Dots Have Their Values: Exploiting The Node-Edge Connections In Graph-based Neural Models For Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 283 | Dynamically Updating Event Representations For Temporal Relation Classification With Multi-category Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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; | emnlp | 2020-11-10 |
| 284 | E-BERT: Efficient-Yet-Effective Entity Embeddings For BERT IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT’s native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors. |
Nina Poerner; Ulli Waltinger; Hinrich Schütze; | emnlp | 2020-11-10 |
| 285 | CODER: Knowledge Infused Cross-lingual Medical Term Embedding for Term Normalization IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 286 | Joint Entity And Relation Extraction With Set Prediction Networks IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 287 | Investigation Of BERT Model On Biomedical Relation Extraction Based On Revised Fine-tuning Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 288 | Logic-guided Semantic Representation Learning For Zero-Shot Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 289 | A Sui Generis QA Approach Using RoBERTa For Adverse Drug Event Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 290 | Towards Accurate And Consistent Evaluation: A Dataset For Distantly-Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 291 | RuREBus: A Case Study Of Joint Named Entity Recognition And Relation Extraction From E-Government Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 292 | WNUT-2020 Task 1 Overview: Extracting Entities And Relations From Wet Lab Protocols Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 293 | RH-Net: Improving Neural Relation Extraction Via Reinforcement Learning and Hierarchical Relational Searching Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 294 | TPLinker: Single-stage Joint Extraction Of Entities And Relations Through Token Pair Linking IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 295 | Meta-Learning For Neural Relation Classification With Distant Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 296 | Effective Distant Supervision for Temporal Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 297 | Learning To Decouple Relations: Few-Shot Relation Classification With Entity-Guided Attention And Confusion-Aware Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 298 | Exploit Multiple Reference Graphs For Semi-supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 299 | T-REX: A Topic-Aware Relation Extraction Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the problem, we propose a Topic-aware Relation EXtraction (T-REX) model. |
Woohwan Jung; Kyuseok Shim; | cikm | 2020-10-19 |
| 300 | The RELX Dataset And Matching The Multilingual Blanks For Cross-Lingual Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 301 | Cross-sentence N-ary Relation Extraction Using Entity Link And Discourse Relation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 302 | Relation Extraction With Self-determined Graph Convolutional Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 303 | Neural Relation Extraction On Wikipedia Tables For Augmenting Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 304 | Mining Infrequent High-Quality Phrases From Domain-Specific Corpora Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on mining high-quality phrases from domain-specific corpora with special consideration of infrequent ones. |
LI WANG et. al. | cikm | 2020-10-19 |
| 305 | Enhance Prototypical Network With Text Descriptions For Few-shot Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 306 | Cross-Lingual Relation Extraction With Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 307 | TDRE: A Tensor Decomposition Based Approach For Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 308 | Named Entity Recognition And Relation Extraction Using Enhanced Table Filling By Contextualized Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 309 | Joint Constrained Learning for Event-Event Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 310 | Video Relation Detection Via Multiple Hypothesis Association IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel relation association method called Multiple Hypothesis Association (MHA). |
ZIXUAN SU et. al. | mm | 2020-10-12 |
| 311 | Visual Relation Of Interest Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 312 | HOSE-Net: Higher Order Structure Embedded Network For Scene Graph Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 313 | Relation Classification As Two-way Span-Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 314 | Improving Long-Tail Relation Extraction With Collaborating Relation-Augmented Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 315 | Semi-supervised Relation Extraction Via Incremental Meta Self-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 316 | Learning From Context Or Names? An Empirical Study On Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 317 | RDSGAN: Rank-based Distant Supervision Relation Extraction With Generative Adversarial Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 318 | DWIE: An Entity-centric Dataset for Multi-task Document-level Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 319 | Clustering-based Unsupervised Generative Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 320 | Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 321 | A Comparative Study On Structural And Semantic Properties Of Sentence Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 322 | AutoRC: Improving BERT Based Relation Classification Models Via Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 323 | Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction! Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 324 | Relation Extraction From Biomedical And Clinical Text: Unified Multitask Learning Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 325 | RECON: Relation Extraction Using Knowledge Graph Context in A Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 326 | Deep Learning Approaches For Extracting Adverse Events And Indications Of Dietary Supplements From Clinical Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 327 | Leveraging Semantic Parsing For Relation Linking Over Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 328 | The Devil Is The Classifier: Investigating Long Tail Relation Classification With Decoupling Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 329 | Cascaded Models For Better Fine-Grained Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present a cascaded approach to labeling fine-grained NER, applying to a newly released fine-grained NER dataset that was used in the TAC KBP 2019 evaluation (Ji et al., 2019), inspired by the fact that training data is available for some of the coarse labels. |
Parul Awasthy; Taesun Moon; Jian Ni; Radu Florian; | arxiv-cs.CL | 2020-09-15 |
| 330 | On Robustness and Bias Analysis of BERT-based Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 331 | Semantic Relations and Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 332 | Relation Detection For Indonesian Language Using Deep Neural Network — Support Vector Machine Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 333 | UPB At SemEval-2020 Task 6: Pretrained Language Models For Definition Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 334 | Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 335 | Meta-Learning with Sparse Experience Replay for Lifelong Language Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 336 | Revisiting LSTM Networks For Semi-Supervised Text Classification Via Mixed Objective Function IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 337 | High-throughput Relation Extraction Algorithm Development Associating Knowledge Articles And Electronic Health Records Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 338 | Extracting Semantic Concepts And Relations From Scientific Publications By Using Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 339 | SemEval-2020 Task 6: Definition Extraction From Free Text With The DEFT Corpus IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 340 | Two Step Joint Model For Drug Drug Interaction Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead of regarding task1 as named entity recognition (NER) task and regarding task2 as relation extraction (RE) task then solving it in a pipeline, we propose a two step joint model to detect DDI and it’s related mentions jointly. |
SILIANG TANG et. al. | arxiv-cs.CL | 2020-08-28 |
| 341 | Entity And Evidence Guided Relation Extraction For DocRED Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 342 | Do Syntax Trees Help Pre-trained Transformers Extract Information? IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 343 | An Annotated Corpus Of Webtables For Information Extraction Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce an annotation framework and a dataset of 217,834 tables from Wikipedia which are annotated with 28 relations, using both classifiers and carefully designed queries over a reference knowledge graph. |
Erin Macdonald; Denilson Barbosa; | arxiv-cs.IR | 2020-08-17 |
| 344 | Distantly Supervised Relation Extraction In Federated Settings Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 345 | Improving Performance Of Relation Extraction Algorithm Via Leveled Adversarial PCNN And Database Expansion Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 346 | NoPropaganda At SemEval-2020 Task 11: A Borrowed Approach To Sequence Tagging And Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 347 | A Relation-Specific Attention Network For Joint Entity And Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. |
YUE YUAN et. al. | ijcai | 2020-07-21 |
| 348 | Attention As Relation: Learning Supervised Multi-head Self-Attention For Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 349 | Modeling Dense Cross-Modal Interactions For Joint Entity-Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 350 | Learning Latent Forests For Medical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 351 | Asking Effective And Diverse Questions: A Machine Reading Comprehension Based Framework For Joint Entity-Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 352 | On The Importance Of Word And Sentence Representation Learning In Implicit Discourse Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 353 | Few-shot Relation Extraction Via Bayesian Meta-learning On Task Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 354 | Few-shot Relation Extraction Via Bayesian Meta-learning On Relation Graphs IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 355 | Pynsett: A Programmable Relation Extractor Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 356 | NLPContributions: An Annotation Scheme For Machine Reading Of Scholarly Contributions In Natural Language Processing Literature Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we describe the outcomes of this pilot annotation phase. |
Jennifer D’Souza; Sören Auer; | arxiv-cs.CL | 2020-06-23 |
| 357 | Implicit Discourse Relation Classification: We Need To Talk About Evaluation IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 358 | ExpBERT: Representation Engineering With Natural Language Explanations IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 359 | Relabel The Noise: Joint Extraction Of Entities And Relations Via Cooperative Multiagents Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 360 | Probing Linguistic Features Of Sentence-Level Representations In Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 361 | ZeroShotCeres: Zero-Shot Relation Extraction From Semi-Structured Webpages IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 362 | Exploiting The Syntax-Model Consistency For Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 363 | Revisiting Unsupervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 364 | Reasoning With Latent Structure Refinement For Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 365 | SpanMlt: A Span-based Multi-Task Learning Framework For Pair-wise Aspect And Opinion Terms Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 366 | Relation Extraction With Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 367 | TACRED Revisited: A Thorough Evaluation Of The TACRED Relation Extraction Task IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 368 | In Layman’s Terms: Semi-Open Relation Extraction From Scientific Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 369 | Dialogue-Based Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 370 | Generalizing Natural Language Analysis Through Span-relation Representations IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 371 | Synchronous Double-channel Recurrent Network For Aspect-Opinion Pair Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 372 | Extracting N-ary Cross-sentence Relations Using Constrained Subsequence Kernel Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 373 | Beyond Short-Term Snippet: Video Relation Detection With Spatio-Temporal Global Context IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 374 | Cascaded Human-Object Interaction Recognition IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 375 | Semantic Loss Application To Entity Relation Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 376 | Relation Of The Relations: A New Paradigm Of The Relation Extraction Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 377 | Benchmarking BioRelEx For Entity Tagging And Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 378 | Recognizing Chinese Judicial Named Entity Using BiLSTM-CRF Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, in this paper, we propose a deep learning-based method named BiLSTM-CRF which consists of bi-directional long short-term memory (BiLSTM) and conditional random fields (CRF). |
PIN TANG et. al. | arxiv-cs.CL | 2020-05-31 |
| 379 | A Comparative Study Of Lexical Substitution Approaches Based On Neural Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a large-scale comparative study of popular neural language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, XLNet, applied to the task of lexical substitution. |
Nikolay Arefyev; Boris Sheludko; Alexander Podolskiy; Alexander Panchenko; | arxiv-cs.CL | 2020-05-29 |
| 380 | Relation Extraction With Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 381 | A Data-driven Approach For Noise Reduction In Distantly Supervised Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 382 | Semi-Automating Knowledge Base Construction For Cancer Genetics Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 383 | A Scientific Information Extraction Dataset For Nature Inspired Engineering Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 384 | In Layman’s Terms: Semi-Open Relation Extraction From Scientific Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 385 | PERLEX: A Bilingual Persian-English Gold Dataset For Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 386 | Relation Adversarial Network For Low Resource Knowledge Graph Completion IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 387 | NERO: A Neural Rule Grounding Framework For Label-Efficient Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 388 | Reasoning With Latent Structure Refinement For Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 389 | LOREM: Language-consistent Open Relation Extraction From Unstructured Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 390 | Adversarial Learning For Supervised And Semi-supervised Relation Extraction In Biomedical Literature Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 391 | An Empirical Study Of Multi-Task Learning On BERT For Biomedical Text Mining IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study a multi-task learning model with multiple decoders on varieties of biomedical and clinical natural language processing tasks such as text similarity, relation extraction, named entity recognition, and text inference. |
Yifan Peng; Qingyu Chen; Zhiyong Lu; | arxiv-cs.CL | 2020-05-06 |
| 392 | PyBART: Evidence-based Syntactic Transformations For IE Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit. |
Aryeh Tiktinsky; Yoav Goldberg; Reut Tsarfaty; | arxiv-cs.CL | 2020-05-04 |
| 393 | Distantly-Supervised Neural Relation Extraction With Side Information Using BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 394 | Exploring The Suitability Of Semantic Spaces As Word Association Models For The Extraction Of Semantic Relationships Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 395 | A Practical Framework For Relation Extraction With Noisy Labels Based On Doubly Transitional Loss Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 396 | MICK: A Meta-Learning Framework For Few-shot Relation Classification With Small Training Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 397 | Contextualised Graph Attention For Improved Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 398 | Learning Relation Ties With A Force-Directed Graph In Distant Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 399 | Improving Neural Relation Extraction With Implicit Mutual Relations IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 400 | Probing Linguistic Features Of Sentence-Level Representations In Neural Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 401 | LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning From Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: On three popular NLP tasks (named entity recognition, relation extraction, sentiment analysis), we find that using this enhanced supervision allows our models to surpass competitive baseline F1 scores by more than 5-10 percentage points, while using 2X times fewer labeled instances. |
DONG-HO LEE et. al. | arxiv-cs.CL | 2020-04-16 |
| 402 | Improving Scholarly Knowledge Representation: Evaluating BERT-based Models For Scientific Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 403 | Relation Transformer Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 404 | Robustly Pre-trained Neural Model For Direct Temporal Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 405 | Downstream Model Design Of Pre-trained Language Model For Relation Extraction Task IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 406 | 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 Orgs Related Experts Details 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 |
| 407 | A Corpus Study And Annotation Schema For Named Entity Recognition And Relation Extraction Of Business Products Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 408 | 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 Orgs Related Experts Details 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 |
| 409 | Efficient Long-distance Relation Extraction With DG-SpanBERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 410 | SelfORE: Self-supervised Relational Feature Learning For Open Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 411 | Named Entities In Medical Case Reports: Corpus And Experiments Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 412 | Common-Knowledge Concept Recognition For SEVA Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 413 | Cross-Lingual Adaptation Using Universal Dependencies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a cross-lingual adaptation method based on syntactic parse trees obtained from the Universal Dependencies (UD), which are consistent across languages, to develop classifiers in low-resource languages. |
Nasrin Taghizadeh; Heshaam Faili; | arxiv-cs.CL | 2020-03-24 |
| 414 | Stanza: A Python Natural Language Processing Toolkit For Many Human Languages IF:6 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. |
Peng Qi; Yuhao Zhang; Yuhui Zhang; Jason Bolton; Christopher D. Manning; | arxiv-cs.CL | 2020-03-16 |
| 415 | Hybrid Attention-Based Transformer Block Model For Distant Supervision Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 416 | Unsupervised Adversarial Domain Adaptation For Implicit Discourse Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 417 | Unique Chinese Linguistic Phenomena Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 418 | Neural Relation Prediction For Simple Question Answering Over Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 419 | Deeper Task-Specificity Improves Joint Entity And Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 420 | Multi-View Consistency For Relation Extraction Via Mutual Information And Structure Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 421 | Neural Snowball For Few-Shot Relation Learning IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 422 | Integrating Relation Constraints With Neural Relation Extractors Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 423 | Simultaneously Linking Entities And Extracting Relations From Biomedical Text Without Mention-Level Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 424 | Are Noisy Sentences Useless For Distant Supervised Relation Extraction? IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 425 | Distilling Knowledge From Well-Informed Soft Labels For Neural Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 426 | Self-Attention Enhanced Selective Gate With Entity-Aware Embedding For Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 427 | Improving Neural Relation Extraction With Positive And Unlabeled Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 428 | Relation Extraction Exploiting Full Dependency Forests Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 429 | Relation Extraction With Convolutional Network Over Learnable Syntax-Transport Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 430 | Localize, Assemble, And Predicate: Contextual Object Proposal Embedding For Visual Relation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 431 | One-Shot Learning For Long-Tail Visual Relation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 432 | Integrating Deep Learning With Logic Fusion For Information Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 433 | Joint Entity And Relation Extraction With A Hybrid Transformer And Reinforcement Learning Based Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 434 | Effective Modeling Of Encoder-Decoder Architecture For Joint Entity And Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 435 | A Neural Architecture For Person Ontology Population Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 436 | BiOnt: Deep Learning Using Multiple Biomedical Ontologies For Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 437 | A Logic-based Relational Learning Approach To Relation Extraction: The OntoILPER System Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 438 | A Neural Approach To Discourse Relation Signal Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 439 | A Novel Document-Level Relation Extraction Method Based on BERT and Entity Information IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 440 | A Comprehensive Exploration of Semantic Relation Extraction Via Pre-trained CNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Qing Li; Lili Li; Weinan Wang; Qi Li; Jiang Zhong; | Knowl. Based Syst. | 2020-01-01 |
| 441 | A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 442 | RUREBUS-2020 SHARED TASK: RUSSIAN RELATION EXTRACTION FOR BUSINESS Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 443 | CFSRE: Context-aware Based on Frame-semantics for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Hongyan Zhao; Ru Li; Xiaoli Li; Hongye Tan; | Knowl. Based Syst. | 2020-01-01 |
| 444 | RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 445 | Relation Classification Via Knowledge Graph Enhanced Transformer Encoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Wenti Huang; Yiyu Mao; Zhan Yang; Lei Zhu; Jun Long; | Knowl. Based Syst. | 2020-01-01 |
| 446 | Can We Survive Without Labelled Data in NLP? Transfer Learning for Open Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 447 | Joint Extraction of Entities and Relations By A Novel End-to-end Model with A Double-pointer Module Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Joint extraction of entities and relations is to detect entities and recognize semantic relations simultaneously. However, some existing joint models predict relations on … |
Chongyou Bai; Limin Pan; Senlin Luo; Zhouting Wu; | Neurocomputing | 2020-01-01 |
| 448 | Auxiliary Learning for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details |
SHENGFEI LYU et. al. | 2020-01-01 | |
| 449 | AGCN: Attention-based Graph Convolutional Networks for Drug-drug Interaction Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Chanhee Park; Jinuk Park; Sanghyun Park; | Expert Syst. Appl. | 2020-01-01 |
| 450 | An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 451 | A Multi-feature Fusion Model for Chinese Relation Extraction with Entity Sense Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 |
| 452 | DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 453 | Graph Convolution Over Multiple Dependency Sub-graphs for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 454 | Threat Intelligence Relationship Extraction Based on Distant Supervision and Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 455 | Mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 456 | Focusing Visual Relation Detection on Relevant Relations with Prior Potentials Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 457 | Structural Block Driven Enhanced Convolutional Neural Representation for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 458 | Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 459 | Dynamic Prototype Selection By Fusing Attention Mechanism for Few-Shot Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 460 | Document-level Relation Extraction with Dual-tier Heterogeneous Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 461 | Graph Enhanced Dual Attention Network for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 462 | A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 463 | ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 464 | Automated Ontology Extraction from Unstructured Texts Using Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Ontologies are computational artifacts to represent knowledge through classes and relations between them. Those knowledge bases require a lot human effort to be constructed due to … |
Raúl Navarro-Almanza; Reyes Juárez-Ramírez; Guillermo Licea Sandoval; Juan R. Castro; | 2020-01-01 | |
| 465 | DAM: Transformer-based Relation Detection for Question Answering Over Knowledge Base Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Yongrui Chen; Huiying Li; | Knowl. Based Syst. | 2020-01-01 |
| 466 | Interactive Learning for Joint Event and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 467 | Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in The Construction of Military Equipment Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 468 | Semantic Relation Extraction Using Sequential and Tree-structured LSTM with Attention IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Semantic relation extraction is crucial to automatically constructing a knowledge graph (KG), and it supports a variety of downstream natural language processing (NLP) … |
Zhiqiang Geng; GuoFei Chen; Yongming Han; Gang Lu; Fang Li; | Inf. Sci. | 2020-01-01 |
| 469 | Chemical-induced Disease Relation Extraction With Dependency Information And Prior Knowledge IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 470 | A Multi-Task Learning Neural Network for Emotion-Cause Pair Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 471 | A Weighted GCN with Logical Adjacency Matrix for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 472 | Biomedical Relation Extraction Using Distant Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 473 | BG-SAC: Entity Relationship Classification Model Based on Self-Attention Supported Capsule Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 |
| 474 | Eliciting Knowledge from Language Models Using Automatically Generated Prompts IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 475 | Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 476 | A Deep Learning Based Method for Extracting Semantic Information from Patent Documents IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: The text-based patent analysis is grounded in information extraction technique. However, such technique suffers from obvious defects such as low degree of automation and … |
LIANG CHEN et. al. | Scientometrics | 2020-01-01 |
| 477 | Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 478 | Biomedical Event Extraction As Multi-turn Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 479 | Large-scale Relation Extraction from Web Documents and Knowledge Graphs with Human-in-the-loop Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Petar Ristoski; Anna Lisa Gentile; Alfredo Alba; Daniel Gruhl; Steve Welch; | J. Web Semant. | 2020-01-01 |
| 480 | Improve Relation Extraction with Dual Attention-guided Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 481 | Extracting Temporal and Causal Relations Based on Event Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Duc-Thuan Vo; Feras Al-Obeidat; Ebrahim Bagheri; | Inf. Process. Manag. | 2020-01-01 |
| 482 | Joint Model of Entity Recognition and Relation Extraction with Self-attention Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 483 | Cross-sentence N-ary Relation Classification Using LSTMs on Graph and Sequence Structures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Lulu Zhao; Weiran Xu; Sheng Gao; Jun Guo; | Knowl. Based Syst. | 2020-01-01 |
| 484 | GREG: A Global Level Relation Extraction with Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 485 | Boundaries and Edges Rethinking: An End-to-end Neural Model for Overlapping Entity Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 |
| 486 | Extraction of A Knowledge Graph from French Cultural Heritage Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 487 | Joint Extraction of Entities and Relations Using Graph Convolution Over Pruned Dependency Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Yin Hong; Yanxia Liu; Suizhu Yang; Kaiwen Zhang; Jianjun Hu; | Neurocomputing | 2020-01-01 |
| 488 | Information Extraction from Text Intensive and Visually Rich Banking Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Berke Oral; Erdem Emekligil; Secil Arslan; Gülsen Eryiǧit; | Inf. Process. Manag. | 2020-01-01 |
| 489 | Argumentative Relation Classification with Background Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 490 | Few-shot Relation Classification By Context Attention-based Prototypical Networks with BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 491 | A Multi-Channel Deep Neural Network for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 492 | A Novel Deep Learning Method for Extracting Unspecific Biomedical Relation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 493 | Relation Extraction for Competitive Intelligence Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 494 | Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 495 | RENERSANs: RELATION EXTRACTION AND NAMED ENTITY RECOGNITION AS SEQUENCE ANNOTATION Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 | |
| 496 | Direction-sensitive Relation Extraction Using Bi-SDP Attention Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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. … |
Hailin Wang; Ke Qin; Guoming Lu; Guangchun Luo; Guisong Liu; | Knowl. Based Syst. | 2020-01-01 |
| 497 | Review of Entity Relation Extraction Methods Related Papers Related Patents Related Grants Related Orgs Related Experts Details |
Li Dongmei; Zhang Yang; Li Dongyuan; Lin Danqiong; | Journal of Computer Research and Development | 2020-01-01 |
| 498 | A Dynamic Parameter Enhanced Network for Distant Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: 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 … |
Yanjie Gou; Yinjie Lei; Lingqiao Liu; Pingping Zhang; Xi Peng; | Knowl. Based Syst. | 2020-01-01 |
| 499 | Heterogeneous Graph Neural Networks for Noisy Few-shot Relation Classification IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Abstract Relation classification is an essential and fundamental task in natural language processing. Distant supervised methods have achieved great success on relation … |
Yuxiang Xie; Hua Xu; Jiaoe Li; Congcong Yang; Kai Gao; | Knowl. Based Syst. | 2020-01-01 |
| 500 | Knowledge-guided Convolutional Networks For Chemical-Disease Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 501 | Combining Context And Knowledge Representations For Chemical-Disease Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 502 | Learning From Explanations With Neural Module Execution Tree Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Neural Modular Execution Tree (NMET) framework for augmenting sequence classification with NL explanations. |
YUJIA QIN et. al. | iclr | 2019-12-21 |
| 503 | The Performance Evaluation Of Multi-representation In The Deep Learning Models For Relation Extraction Task Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 504 | Effective Attention Modeling For Neural Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 505 | Enhancing Relation Extraction Using Syntactic Indicators And Sentential Contexts Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 506 | Are Noisy Sentences Useless For Distant Supervised Relation Extraction? IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 507 | Improving Distant Supervised Relation Extraction By Dynamic Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 508 | Leveraging Dependency Forest For Neural Medical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate a method to alleviate this problem by utilizing dependency forests. |
LINFENG SONG et. al. | arxiv-cs.CL | 2019-11-11 |
| 509 | Towards Understanding Gender Bias In Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We create WikiGenderBias, a distantly supervised dataset with a human annotated test set. |
ANDREW GAUT et. al. | arxiv-cs.LG | 2019-11-09 |
| 510 | E-BERT: Efficient-Yet-Effective Entity Embeddings For BERT IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT’s native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors. |
Nina Poerner; Ulli Waltinger; Hinrich Schütze; | arxiv-cs.CL | 2019-11-09 |
| 511 | Relation Adversarial Network For Low Resource Knowledge Graph Completion IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 512 | Learning From Explanations With Neural Execution Tree IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Neural Execution Tree (NExT) framework to augment training data for text classification using NL explanations. |
ZIQI WANG et. al. | arxiv-cs.CL | 2019-11-04 |
| 513 | On The Effectiveness Of The Pooling Methods For Biomedical Relation Extraction With Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 514 | Nested Relation Extraction With Iterative Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 515 | Large Margin Prototypical Network For Few-shot Relation Classification With Fine-grained Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 516 | Enriching Pre-trained Language Model With Entity Information For Relation Classification IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 517 | A Fine-grained And Noise-aware Method For Neural Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 518 | Connecting The Dots: Document-level Neural Relation Extraction With Edge-oriented Graphs IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 519 | Next Sentence Prediction Helps Implicit Discourse Relation Classification Within And Across Domains IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 520 | An Improved Neural Baseline For Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new neural system that achieves about 10% absolute improvement in accuracy over the previous best system (25% error reduction) on two benchmark datasets. |
Qiang Ning; Sanjay Subramanian; Dan Roth; | emnlp | 2019-11-02 |
| 521 | Cross-lingual Structure Transfer For Relation And Event Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate the suitability of cross-lingual structure transfer techniques for these tasks. |
ANANYA SUBBURATHINAM et. al. | emnlp | 2019-11-02 |
| 522 | Rewarding Coreference Resolvers For Being Consistent With World Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 523 | Nearly-Unsupervised Hashcode Representations For Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 524 | Open Relation Extraction: Relational Knowledge Transfer From Supervised Data To Unsupervised Data IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 525 | Looking Beyond Label Noise: Shifted Label Distribution Matters In Distantly Supervised Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 526 | Weakly Supervised Cross-lingual Semantic Relation Classification Via Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 527 | Easy First Relation Extraction With Information Redundancy Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 528 | Neural Cross-Lingual Relation Extraction Based On Bilingual Word Embedding Mapping Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 529 | Cross-Sentence N-ary Relation Extraction Using Lower-Arity Universal Schemas Related Papers Related Patents |