Paper Digest: Recent Papers on Relation Extraction
Paper Digest Team extracted all recent Relation Extraction related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic.
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TABLE 1: Paper Digest: Recent Papers on Relation Extraction
| Paper | Author(s) | Source | Date | |
|---|---|---|---|---|
| 1 | Biomedical Relation Extraction Via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a document-level Bio-RE framework via LLM Adaptive Document-Relation Cross-Mapping (ADRCM) Fine-Tuning and Concept Unique Identifier (CUI) Retrieval-Augmented Generation (RAG). |
Yufei Shang; Yanrong Guo; Shijie Hao; Richang Hong; | arxiv-cs.CL | 2025-01-09 |
| 2 | GLiREL — Generalist Model for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce GLiREL (Generalist Lightweight model for zero-shot Relation Extraction), an efficient architecture and training paradigm for zero-shot relation classification. |
Jack Boylan; Chris Hokamp; Demian Gholipour Ghalandari; | arxiv-cs.CL | 2025-01-06 |
| 3 | CPTuning: Contrastive Prompt Tuning for Generative Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although having achieved promising performance, existing approaches assume only one deterministic relation between each pair of entities without considering real scenarios where multiple relations may be valid, i.e., entity pair overlap, causing their limited applications. To address this problem, we introduce a novel contrastive prompt tuning method for RE, CPTuning, which learns to associate a candidate relation between two in-context entities with a probability mass above or below a threshold, corresponding to whether the relation exists. |
Jiaxin Duan; Fengyu Lu; Junfei Liu; | arxiv-cs.CL | 2025-01-04 |
| 4 | Rethinking Relation Extraction: Beyond Shortcuts to Generalization with A Debiased Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To establish a new baseline on DREB, we introduce MixDebias, a debiasing method combining data-level and model training-level techniques. |
LIANG HE et. al. | arxiv-cs.AI | 2025-01-02 |
| 5 | KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing Doc-RE methods focus on optimizing single reasoning ability, but lack the ability to utilize external knowledge for comprehensive reasoning on long documents. To solve these problems, a knowledge retrieval augmented method, named KnowRA, was proposed with comprehensive reasoning to autonomously determine whether to accept external knowledge to assist DocRE. |
Chengcheng Mai; Yuxiang Wang; Ziyu Gong; Hanxiang Wang; Yihua Huang; | arxiv-cs.CL | 2024-12-31 |
| 6 | AmalREC: A Dataset for Relation Extraction and Classification Leveraging Amalgamation of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a generic framework to generate well-structured sentences from given tuples with the help of Large Language Models (LLMs). |
Pranshu Pandya; Mahek Bhavesh Vora; Soumya Bharadwaj; Ashish Anand; | arxiv-cs.IR | 2024-12-29 |
| 7 | Logic Induced High-Order Reasoning Network for Event-Event Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel method named LogicERE, which performs high-order event relation reasoning through modeling logic constraints. |
Peixin Huang; Xiang Zhao; Minghao Hu; Zhen Tan; Weidong Xiao; | arxiv-cs.IT | 2024-12-19 |
| 8 | EventFull: Complete and Consistent Event Relation Annotation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In response, we introduce \textit{EventFull}, the first tool that supports consistent, complete and efficient annotation of temporal, causal and coreference relations via a unified and synergetic process. |
Alon Eirew; Eviatar Nachshoni; Aviv Slobodkin; Ido Dagan; | arxiv-cs.CL | 2024-12-17 |
| 9 | VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance data from the embedding space. |
Khai Phan Tran; Wen Hua; Xue Li; | arxiv-cs.CL | 2024-12-17 |
| 10 | Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To efficiently select optimal dataset combinations for enhancing multi-task learning (MTL) performance in large language models, we proposed a novel framework that leverages a neural network to predict the best dataset combinations. |
Zaifu Zhan; Rui Zhang; | arxiv-cs.CL | 2024-12-16 |
| 11 | Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, upon analyzing existing prompt-based approaches for CRE, we identified several critical limitations, such as inaccurate prompt selection, inadequate mechanisms for mitigating forgetting in shared parameters, and suboptimal handling of cross-task and within-task variances. To overcome these challenges, we draw inspiration from the relationship between prefix-tuning and mixture of experts, proposing a novel approach that employs a prompt pool for each task, capturing variations within each task while enhancing cross-task variances. |
MINH LE et. al. | arxiv-cs.CL | 2024-12-11 |
| 12 | Enhancing Relation Extraction Via Supervised Rationale Verification and Feedback Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the rapid progress that existing automated feedback methods have made in correcting the output of large language models (LLMs), these methods cannot be well applied to the relation extraction (RE) task due to their designated feedback objectives and correction manner. To address this problem, we propose a novel automated feedback framework for RE, which presents a rationale supervisor to verify the rationale and provides re-selected demonstrations as feedback to correct the initial prediction. |
YONGQI LI et. al. | arxiv-cs.CL | 2024-12-10 |
| 13 | Diversity Over Quantity: A Lesson From Few Shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we demonstrate that training on a diverse set of relations significantly enhances a model’s ability to generalize to unseen relations, even when the overall dataset size remains fixed. |
Amir DN Cohen; Shauli Ravfogel; Shaltiel Shmidman; Yoav Goldberg; | arxiv-cs.CL | 2024-12-06 |
| 14 | A Survey on Cutting-edge Relation Extraction Techniques Based on Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Linguistics (ACL) conferences over the past four years, focusing on models that leverage language models. |
Jose A. Diaz-Garcia; Julio Amador Diaz Lopez; | arxiv-cs.CL | 2024-11-27 |
| 15 | LogicST: A Logical Self-Training Framework for Document-Level Relation Extraction with Incomplete Annotations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods are purely black-box and sub-symbolic, making them difficult to interpret and prone to overlooking symbolic interdependencies between relations. To remedy this deficiency, our insight is that symbolic knowledge, such as logical rules, can be used as diagnostic tools to identify conflicts between pseudo-labels. |
Shengda Fan; Yanting Wang; Shasha Mo; Jianwei Niu; | emnlp | 2024-11-11 |
| 16 | Bio-RFX: Refining Biomedical Extraction Via Advanced Relation Classification and Structural Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Biomedical Relation-First eXtraction (Bio-RFX) model by leveraging sentence-level relation classification before entity extraction to tackle entity ambiguity. |
MINJIA WANG et. al. | emnlp | 2024-11-11 |
| 17 | SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we release a new entity and relation extraction dataset for entities related to datasets, methods, and tasks in scientific articles. |
QI ZHANG et. al. | emnlp | 2024-11-11 |
| 18 | GDTB: Genre Diverse Data for English Shallow Discourse Parsing Across Modalities, Text Types, and Domains Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present and evaluate a new open-access, multi-genre benchmark for PDTB-style shallow discourse parsing, based on the existing UD English GUM corpus, for which discourse relation annotations in other frameworks already exist. |
YANG JANET LIU et. al. | emnlp | 2024-11-11 |
| 19 | SRF: Enhancing Document-Level Relation Extraction with A Novel Secondary Reasoning Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel Secondary Reasoning Framework (SRF) for DocRE. |
FU ZHANG et. al. | emnlp | 2024-11-11 |
| 20 | Preserving Generalization of Language Models in Few-shot Continual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a novel method that leverages often-discarded language model heads. |
QUYEN TRAN et. al. | emnlp | 2024-11-11 |
| 21 | Argument Relation Classification Through Discourse Markers and Adversarial Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce DISARM, which advances the state of the art with a training procedure combining multi-task and adversarial learning strategies. |
Michele Luca Contalbo; Francesco Guerra; Matteo Paganelli; | emnlp | 2024-11-11 |
| 22 | Will LLMs Replace The Encoder-Only Models in Temporal Relation Classification? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate LLMs’ performance and decision process in the Temporal Relation Classification task. |
Gabriel Roccabruna; Massimo Rizzoli; Giuseppe Riccardi; | emnlp | 2024-11-11 |
| 23 | Grasping The Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce REPaL, comprising three stages: (1) We leverage large language models (LLMs) to generate initial seed instances from relation definitions and an unlabeled corpus. |
Sizhe Zhou; Yu Meng; Bowen Jin; Jiawei Han; | emnlp | 2024-11-11 |
| 24 | Multi-Level Cross-Modal Alignment for Speech Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we construct two real SpeechRE datasets to facilitate subsequent researches and propose a Multi-level Cross-modal Alignment Model (MCAM) for SpeechRE. |
LIANG ZHANG et. al. | emnlp | 2024-11-11 |
| 25 | Topic-Oriented Open Relation Extraction with A Priori Seed Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve topic-oriented ORE, we propose a zero-shot approach called PriORE: Open Relation Extraction with a Priori seed generation. |
Linyi Ding; Jinfeng Xiao; Sizhe Zhou; Chaoqi Yang; Jiawei Han; | emnlp | 2024-11-11 |
| 26 | Modeling Layout Reading Order As Ordering Relations for Visually-rich Document Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we argue that this formulation does not adequately convey the complete reading order information in the layout, which may potentially lead to performance decline in downstream tasks. To address this issue, we propose to model the layout reading order as ordering relations over the set of layout elements, which have sufficient expressive capability for the complete reading order information. |
CHONG ZHANG et. al. | emnlp | 2024-11-11 |
| 27 | Beyond The Numbers: Transparency in Relation Extraction Benchmark Creation and Leaderboards Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper investigates the transparency in the creation of benchmarks and the use of leaderboards for measuring progress in NLP, with a focus on the relation extraction (RE) task. |
Varvara Arzt; Allan Hanbury; | arxiv-cs.CL | 2024-11-07 |
| 28 | Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we represent the structured output as a graph-style triplet rather than natural language expressions and leverage generative LLMs for the DocRE task. |
TAO ZHANG et. al. | arxiv-cs.CL | 2024-11-05 |
| 29 | Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces a novel approach to sentence-level relation extraction (RE) that integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) to generate contextually enriched support documents. |
Vicky Dong; Hao Yu; Yao Chen; | arxiv-cs.CL | 2024-10-30 |
| 30 | BioNCERE: Non-Contrastive Enhancement For Relation Extraction In Biomedical Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the present paper, a new training method called biological non-contrastive relation extraction (BioNCERE) is introduced for relation extraction without using any named entity labels for training to reduce annotation costs. |
Farshad Noravesh; | arxiv-cs.CL | 2024-10-30 |
| 31 | Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel framework based on Gaussian prototype and adaptive margin named GPAM for FsRE with NOTA, which includes three modules, semi-factual representation, GMM-prototype metric learning and decision boundary learning. |
TIANLIN GUO et. al. | arxiv-cs.CV | 2024-10-26 |
| 32 | Local and Global Graph Modeling with Edge-weighted Graph Attention Network for Handwritten Mathematical Expression Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel approach to Handwritten Mathematical Expression Recognition (HMER) by leveraging graph-based modeling techniques. |
Yejing Xie; Richard Zanibbi; Harold Mouchère; | arxiv-cs.CV | 2024-10-24 |
| 33 | XCrowd: Combining Explainability and Crowdsourcing to Diagnose Models in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a new method for diagnosing model predictions and detecting potential inaccuracies. |
Alisa Smirnova; Jie Yang; Philippe Cudre-Mauroux; | cikm | 2024-10-21 |
| 34 | Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present MulCo : Distilling Mul ti-Scale Knowledge via Co ntrastive Learning, a knowledge co-distillation approach that shares knowledge across multiple event pair proximity bands to improve performance on all types of temporal datasets. |
Hao-Ren Yao; Luke Breitfeller; Aakanksha Naik; Chunxiao Zhou; Carolyn Rose; | cikm | 2024-10-21 |
| 35 | Document-Level Relation Extraction Based on Heterogeneous Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an efficient Document-Level Relation Extraction Model based on Heterogeneous Graph Reasoning (HGR-DREM), which enables relation extraction more accurate. |
Dong Li; Miao Li; Zhilei Lei; Baoyan Song; Xiaohuan Shan; | cikm | 2024-10-21 |
| 36 | AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence, leading to the poor performance of pure text-in, text-out language models (LMs). To address these challenges, in this paper, we propose an agent-based RE framework, namely AgentRE, which fully leverages the potential of large language models (LLMs) including memory, retrieval and reflection, to achieve RE in complex scenarios. |
Yuchen Shi; Guochao Jiang; Tian Qiu; Deqing Yang; | cikm | 2024-10-21 |
| 37 | CAG: A Consistency-Adaptive Text-Image Alignment Generation for Joint Multimodal Entity-Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Consistency-adaptive text-image Alignment Generation (CAG) framework for various text-image consistency scenarios. |
XINJIE YANG et. al. | cikm | 2024-10-21 |
| 38 | Subversive Characters and Stereotyping Readers: Characterizing Queer Relationalities with Dialogue-Based Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Built on cognitive stylistics, linguistic anthropology, and dialogue relation extraction, in this paper, we attempt to model the cognitive process of stereotyping TV characters in dialogic interactions. |
Kent K. Chang; Anna Ho; David Bamman; | arxiv-cs.CL | 2024-10-19 |
| 39 | Few-Shot Joint Multimodal Entity-Relation Extraction Via Knowledge-Enhanced Cross-modal Prompt Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the insufficient information in the few-shot setting, we introduce the \textbf{K}nowledge-\textbf{E}nhanced \textbf{C}ross-modal \textbf{P}rompt \textbf{M}odel (KECPM) for JMERE. |
Li Yuan; Yi Cai; Junsheng Huang; | arxiv-cs.CL | 2024-10-18 |
| 40 | The Effects of Hallucinations in Synthetic Training Data for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we examine the effects of hallucinations on the performance of relation extraction on the document and sentence levels. |
Steven Rogulsky; Nicholas Popovic; Michael Färber; | arxiv-cs.CL | 2024-10-10 |
| 41 | Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Knowledge-guided binary Question Answering (KnowQA) method with event structures for ECRE, consisting of two stages: Event Structure Construction and Binary Question Answering. |
Zimu Wang; Lei Xia; Wei Wang; Xinya Du; | arxiv-cs.CL | 2024-10-07 |
| 42 | Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary Relational Knowledge Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the construction of NKGs remains at a course-grained level, which is always in a single schema, ignoring the order of entities and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. |
HAORAN LUO et. al. | nips | 2024-10-07 |
| 43 | Unleashing The Power of Large Language Models in Zero-shot Relation Extraction Via Self-Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current methods often perform suboptimally, mainly due to a lack of detailed, context-specific prompts needed for understanding various sentences and relations. To address this, we introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within LLMs. |
Siyi Liu; Yang Li; Jiang Li; Shan Yang; Yunshi Lan; | arxiv-cs.IR | 2024-10-01 |
| 44 | TPN: Transferable Proto-Learning Network Towards Few-shot Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a Transferable Proto-Learning Network (TPN) to address the challenging issue. |
Yu Zhang; Zhao Kang; | arxiv-cs.CL | 2024-10-01 |
| 45 | Multi-Granularity Sparse Relationship Matrix Prediction Network for End-to-End Scene Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a sparse relationship matrix that bridges entity detection and relation detection. |
lei wang; Zejian Yuan; Badong Chen; | eccv | 2024-09-30 |
| 46 | DiMB-RE: Mining The Scientific Literature for Diet-Microbiome Associations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We developed DiMB-RE, a comprehensive corpus annotated with 15 entity types (e.g., Nutrient, Microorganism) and 13 relation types (e.g., increases, improves) capturing diet-microbiome associations. |
Gibong Hong; Veronica Hindle; Nadine M. Veasley; Hannah D. Holscher; Halil Kilicoglu; | arxiv-cs.CL | 2024-09-29 |
| 47 | Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce Konstruktor – an efficient and robust approach that breaks down the problem into three steps: (i) entity extraction and entity linking, (ii) relation prediction, and (iii) querying the knowledge graph. |
Maria Lysyuk; Mikhail Salnikov; Pavel Braslavski; Alexander Panchenko; | arxiv-cs.CL | 2024-09-24 |
| 48 | Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. |
Xin Wang; Xinyi Bai; | arxiv-cs.CL | 2024-09-15 |
| 49 | Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on extensive experiments conducted using 15 state-of-the-art relation extraction algorithms ranging from recurrent architectures to large language models and seven large-scale datasets, this research suggests that modern relation extractors are not robust to complex data and relation characteristics. |
Anushka Swarup; Avanti Bhandarkar; Olivia P. Dizon-Paradis; Ronald Wilson; Damon L. Woodard; | arxiv-cs.CL | 2024-09-07 |
| 50 | Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider few-shot learning is of great practical significance to RC and thus improve a modern framework of metric learning for few-shot RC. |
Miao Fan; Yeqi Bai; Mingming Sun; Ping Li; | arxiv-cs.CL | 2024-09-05 |
| 51 | A Graph Propagation Model with Rich Event Structures for Joint Event Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
JUNCHI ZHANG et. al. | Inf. Process. Manag. | 2024-09-01 |
| 52 | ViRED: Prediction of Visual Relations in Engineering Drawings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the field of visual relation detection, the structure of the task inherently limits its capacity to assess relationships among all entity pairs in the drawings. To address this issue, we propose a vision-based relation detection model, named ViRED, to identify the associations between tables and circuits in electrical engineering drawings. |
Chao Gu; Ke Lin; Yiyang Luo; Jiahui Hou; Xiang-Yang Li; | arxiv-cs.CV | 2024-09-01 |
| 53 | LLM with Relation Classifier for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Large language models (LLMs) have created a new paradigm for natural language processing. |
Xingzuo Li; Kehai Chen; Yunfei Long; Min Zhang; | arxiv-cs.CL | 2024-08-25 |
| 54 | End-to-end Learning of Logical Rules for Enhancing Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these learning pipelines may suffer from the issue of error propagation. To mitigate this issue, we propose Joint Modeling Relation extraction and Logical rules or JMRL for short, a novel rule-based framework that jointly learns both a DocRE model and logical rules in an end-to-end fashion. |
Kunxun Qi; Jianfeng Du; Hai Wan; | acl | 2024-08-20 |
| 55 | Improving Large Language Models in Event Relation Logical Prediction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we conduct an in-depth investigation to systematically explore the capability of LLMs in understanding and applying event relation logic. |
MEIQI CHEN et. al. | acl | 2024-08-20 |
| 56 | Reward-based Input Construction for Cross-document Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Given the nature of long documents in cross-document RE, extracting document embeddings is challenging due to the length constraints of pre-trained language models. Therefore, we propose REward-based Input Construction (REIC), the first learning-based sentence selector for cross-document RE. |
Byeonghu Na; Suhyeon Jo; Yeongmin Kim; Il-chul Moon; | acl | 2024-08-20 |
| 57 | Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Mixture Learner for Partially Annotated Classification (MLPAC), an RL-based framework combining the exploration ability of reinforcement learning and the exploitation ability of supervised learning. |
Zixia Jia; Junpeng Li; Shichuan Zhang; Anji Liu; Zilong Zheng; | acl | 2024-08-20 |
| 58 | TTM-RE: Memory-Augmented Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To unlock the full potential of large-scale noisy training data for document-level relation extraction, we propose TTM-RE, a novel approach that integrates a trainable memory module, known as the Token Turing Machine, with a noisy-robust loss function that accounts for the positive-unlabeled setting. |
Chufan Gao; Xuan Wang; Jimeng Sun; | acl | 2024-08-20 |
| 59 | Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recognizing the prevalence of noisy labels in real-world datasets, we introduce a more practical learning scenario, termed as noisy-CRE. In response to this challenge, we propose a noise-resistant contrastive framework called Noise-guided Attack in Contrastive Learning (NaCL), aimed at learning incremental corrupted relations. |
TING WU et. al. | acl | 2024-08-20 |
| 60 | Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the observation, we propose a frustratingly easy method called SEQ* for IL with PLMs. |
Junhao Zheng; Shengjie Qiu; Qianli Ma; | acl | 2024-08-20 |
| 61 | When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new idea for OpenRE in the era of LLMs, that is, extracting relational phrases and directly exploiting the knowledge in LLMs to assess the semantic similarity between phrases without relying on any additional metrics. |
JIAXIN WANG et. al. | acl | 2024-08-20 |
| 62 | VrdONE: One-stage Video Visual Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Addressing the need to recognize entity pairs’ spatiotemporal interactions across a range of durations, we propose VrdONE, a streamlined yet efficacious one-stage model. |
XINJIE JIANG et. al. | arxiv-cs.CV | 2024-08-18 |
| 63 | Multimodal Relational Triple Extraction with Query-based Entity Object Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the limitation, we propose a novel task, Multimodal Entity-Object Relational Triple Extraction, which aims to extract all triples (entity span, relation, object region) from image-text pairs. |
LEI HEI et. al. | arxiv-cs.IR | 2024-08-16 |
| 64 | Only One Relation Possible? Modeling The Ambiguity in Event Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, instead of directly making predictions on \textit{Vague}, we propose a multi-label classification solution for ETRE (METRE) to infer the possibility of each temporal relation independently, where we treat \textit{Vague} as the cases when there is more than one possible relation between two events. |
Yutong Hu; Quzhe Huang; Yansong Feng; | arxiv-cs.CL | 2024-08-14 |
| 65 | Generalized Knowledge-enhanced Framework for Biomedical Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This research effort aims to address the accelerating growth in biomedical publications and the intricate nature of biomedical texts, which are written for mainly domain experts. |
Minh Nguyen; Phuong Le; | arxiv-cs.CL | 2024-08-13 |
| 66 | A Few-Shot Approach for Relation Extraction Domain Adaptation Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we experiment with leveraging in-context learning capabilities of Large Language Models to perform schema-constrained data annotation, collecting in-domain training instances for a Transformer-based relation extraction model deployed on titles and abstracts of research papers in the Architecture, Construction, Engineering and Operations (AECO) domain. |
Vanni Zavarella; Juan Carlos Gamero-Salinas; Sergio Consoli; | arxiv-cs.CL | 2024-08-05 |
| 67 | Dialogue Ontology Relation Extraction Via Constrained Chain-of-Thought Decoding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on relation extraction in a transfer learning set-up. |
RENATO VUKOVIC et. al. | arxiv-cs.CL | 2024-08-05 |
| 68 | Empirical Analysis of Dialogue Relation Extraction with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Interestingly, we discover that LLMs significantly alleviate two issues in existing DRE methods. |
GUOZHENG LI et. al. | ijcai | 2024-08-03 |
| 69 | Attention Based Document-level Relation Extraction with None Class Ranking Loss Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, current methods only independently estimate the cases of predefined relations, ignoring the case of "no relation”, which results in poor prediction. To address the above issues, we propose a document-level RE method based on attention mechanisms, which considers the case of "no relation”. |
XIAOLONG XU et. al. | ijcai | 2024-08-03 |
| 70 | Making LLMs As Fine-Grained Relation Extraction Data Augmentor Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the extensive generative capabilities of large language models (LLMs), we introduce a novel framework named ConsistRE, aiming to maintain context consistency in RE. |
YIFAN ZHENG et. al. | ijcai | 2024-08-03 |
| 71 | Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce Micre (Meta In-Context learning of LLMs for Relation Extraction), a new meta-training framework for zero and few-shot RE where an LLM is tuned to do ICL on a diverse collection of RE datasets (i.e., learning to learn in context for RE). |
GUOZHENG LI et. al. | ijcai | 2024-08-03 |
| 72 | KnowledgeHub: An End-to-End Tool for Assisted Scientific Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. |
SHINNOSUKE TANAKA et. al. | ijcai | 2024-08-03 |
| 73 | ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on An Academic Budget Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever module undertakes the identification of candidate entities or relations that could potentially appear within the text. |
Riccardo Orlando; Pere-Lluis Huguet Cabot; Edoardo Barba; Roberto Navigli; | arxiv-cs.CL | 2024-07-31 |
| 74 | GEGA: Graph Convolutional Networks and Evidence Retrieval Guided Attention for Enhanced Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, DocRE suffers from two challenges: firstly, the relevance between evidence and entity pairs is weak; secondly, there is insufficient extraction of complex cross-relations between long-distance multi-entities. To overcome these challenges, we propose GEGA, a novel model for DocRE. |
YANXU MAO et. al. | arxiv-cs.CL | 2024-07-31 |
| 75 | Event-Arguments Extraction Corpus and Modeling Using BERT for Arabic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the \hadath corpus ($550$k tokens) as an extension of Wojood, enriched with event-argument annotations. |
Alaa Aljabari; Lina Duaibes; Mustafa Jarrar; Mohammed Khalilia; | arxiv-cs.CL | 2024-07-30 |
| 76 | Are LLMs Good Annotators for Discourse-level Event Relation Extraction? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we assess the effectiveness of LLMs in addressing discourse-level ERE tasks characterized by lengthy documents and intricate relations encompassing coreference, temporal, causal, and subevent types. |
Kangda Wei; Aayush Gautam; Ruihong Huang; | arxiv-cs.CL | 2024-07-28 |
| 77 | Document-level Clinical Entity and Relation Extraction Via Knowledge Base-Guided Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Generative pre-trained transformer (GPT) models have shown promise in clinical entity and relation extraction tasks because of their precise extraction and contextual understanding capability. |
Kriti Bhattarai; Inez Y. Oh; Zachary B. Abrams; Albert M. Lai; | arxiv-cs.CL | 2024-07-13 |
| 78 | Empowering Few-Shot Relation Extraction with The Integration of Traditional RE Methods and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Traditional RE models are hampered by a lack of necessary prior knowledge, while LLMs fall short in their task-specific capabilities for RE. To address these shortcomings, we propose a Dual-System Augmented Relation Extractor (DSARE), which synergistically combines traditional RE models with LLMs. |
YE LIU et. al. | arxiv-cs.CL | 2024-07-11 |
| 79 | Consistent Document-Level Relation Extraction Via Counterfactuals Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: It has been shown that RE models trained on real-world data suffer from factual biases. To evaluate and address this issue, we present CovEReD, a counterfactual data generation approach for document-level relation extraction datasets using entity replacement. |
Ali Modarressi; Abdullatif Köksal; Hinrich Schütze; | arxiv-cs.CL | 2024-07-09 |
| 80 | A Global-Local Attention Mechanism for Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous studies have predominantly focused on integrating the attention mechanism into relation classification at a global scale, overlooking the importance of the local context. To address this gap, this paper introduces a novel global-local attention mechanism for relation classification, which enhances global attention with a localized focus. |
Yiping Sun; | arxiv-cs.CL | 2024-07-01 |
| 81 | Focused Contrastive Loss for Classification With Pre-Trained Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Contrastive learning, which learns data representations by contrasting similar and dissimilar instances, has achieved great success in various domains including natural language … |
JIAYUAN HE et. al. | IEEE Transactions on Knowledge and Data Engineering | 2024-07-01 |
| 82 | Augmenting Document-level Relation Extraction with Efficient Multi-Supervision Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Despite its popularity in sentence-level relation extraction, distantly supervised data is rarely utilized by existing work in document-level relation extraction due to its noisy … |
Xiangyu Lin; Weijia Jia; Zhiguo Gong; | arxiv-cs.CL | 2024-07-01 |
| 83 | Defying Forgetting in Continual Relation Extraction Via Batch Spectral Norm Regularization Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Continual relation extraction (CRE) aims at incrementally training the model with new relations without forgetting the old ones. Recently, various methods, relying on the stored … |
Rundong Gao; Wenkai Yang; Xu Sun; | 2024 International Joint Conference on Neural Networks … | 2024-06-30 |
| 84 | MKDAT: Multi-Level Knowledge Distillation with Adaptive Temperature for Distantly Supervised Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distantly supervised relation extraction (DSRE), first used to address the limitations of manually annotated data via automatically annotating the data with triplet facts, is … |
Jun Long; Zhuoying Yin; Yan Han; Wenti Huang; | Inf. | 2024-06-30 |
| 85 | Implicit Discourse Relation Classification For Nigerian Pidgin Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on Nigerian Pidgin (NP), which is spoken by nearly 100 million people, but has comparatively very few NLP resources and corpora. |
Muhammed Saeed; Peter Bourgonje; Vera Demberg; | arxiv-cs.CL | 2024-06-26 |
| 86 | Towards Better Graph-based Cross-document Relation Extraction Via Non-bridge Entity Enhancement and Prediction Debiasing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the commonly-used dataset–CodRED contains substantial NA instances, leading to the prediction bias during inference. To address these issues, in this paper, we propose a novel graph-based cross-document RE model with non-bridge entity enhancement and prediction debiasing. |
HAO YUE et. al. | arxiv-cs.CL | 2024-06-24 |
| 87 | LayoutPointer: A Spatial-Context Adaptive Pointer Network for Visual Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Firstly, most existing models inadequately utilize spatial information of entities, often failing to predict connections or incorrectly linking spatially distant entities. Secondly, the improper input order of tokens challenges in extracting complete entity pairs from documents with multi-line entities when text is extracted via PDF parser or OCR. To address these challenges, we propose LayoutPointer, a Spatial-Context Adaptive Pointer Network. LayoutPointer explicitly enhances spatial-context relationships by incorporating 2D relative position information and adaptive spatial constraints within self-attention. |
Huang Siyuan; Yongping Xiong; Wu Guibin; | naacl | 2024-06-20 |
| 88 | DuRE: Dual Contrastive Self Training for Semi-Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing ST methods in RE fail to tackle the challenge of long-tail relations. In this work, we propose DuRE, a novel ST framework to tackle these problems. |
Yuxi Feng; Laks Lakshmanan; | naacl | 2024-06-20 |
| 89 | GenRES: Rethinking Evaluation for Generative Relation Extraction in The Era of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This shortfall arises because these metrics rely on exact matching with human-annotated reference relations, while GRE methods often produce diverse and semantically accurate relations that differ from the references. To fill this gap, we introduce GenRES for a multi-dimensional assessment in terms of the topic similarity, uniqueness, granularity, factualness, and completeness of the GRE results. |
Pengcheng Jiang; Jiacheng Lin; Zifeng Wang; Jimeng Sun; Jiawei Han; | naacl | 2024-06-20 |
| 90 | RE2: Region-Aware Relation Extraction from Visually Rich Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose REgion-Aware Relation Extraction (\bf{RE^2}) that leverages region-level spatial structure among the entity blocks to improve their relation prediction. |
Pritika Ramu; Sijia Wang; Lalla Mouatadid; Joy Rimchala; Lifu Huang; | naacl | 2024-06-20 |
| 91 | Clear Up Confusion: Advancing Cross-Domain Few-Shot Relation Extraction Through Relation-Aware Prompt Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a relation-aware prompt learning method with pre-training. |
GE BAI et. al. | naacl | 2024-06-20 |
| 92 | Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work explores the performance of fine-tuned LLMs and their integration into the Retrieval Augmented-based (RAG) RE approach to address the challenges of identifying implicit relations at the sentence level, particularly when LLMs act as generators within the RAG framework. |
Sefika Efeoglu; Adrian Paschke; | arxiv-cs.CL | 2024-06-20 |
| 93 | Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost, and fuses coarse-grained recall and fine-grained classification for rich interactions with guaranteed inference speed. |
SHILONG LI et. al. | naacl | 2024-06-20 |
| 94 | TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods still struggle with limited and unevenly distributed annotated data. Therefore, inspired by the abundant global knowledge stored within pre-trained language models (PLMs), we propose a multi-task prompt learning framework for TRE (TemPrompt), incorporating prompt tuning and contrastive learning to tackle these issues. |
JING YANG et. al. | arxiv-cs.CL | 2024-06-20 |
| 95 | Efficient Information Extraction in Few-Shot Relation Classification Through Contrastive Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning. |
Philipp Borchert; Jochen De Weerdt; Marie-Francine Moens; | naacl | 2024-06-20 |
| 96 | Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost, and fuses coarse-grained recall and fine-grained classification for rich interactions with guaranteed inference speed. |
SHILONG LI et. al. | arxiv-cs.CL | 2024-06-17 |
| 97 | Analysing Zero-shot Temporal Relation Extraction on Clinical Notes Using Temporal Consistency Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents the first study for temporal relation extraction in a zero-shot setting focusing on biomedical text. |
Vasiliki Kougia; Anastasiia Sedova; Andreas Stephan; Klim Zaporojets; Benjamin Roth; | arxiv-cs.CL | 2024-06-17 |
| 98 | How Good Are LLMs at Relation Extraction Under Low-Resource Scenario? Comprehensive Evaluation Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation Extraction (RE) serves as a crucial technology for transforming unstructured text into structured information, especially within the framework of Knowledge Graph … |
Dawulie Jinensibieke; Mieradilijiang Maimaiti; Wentao Xiao; Yuanhang Zheng; Xiaobo Wang; | arxiv-cs.CL | 2024-06-16 |
| 99 | GLiNER Multi-task: Generalist Lightweight Model for Various Information Extraction Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this article, we will introduce a new kind of GLiNER model that can be used for various information extraction tasks while being a small encoder model. |
Ihor Stepanov; Mykhailo Shtopko; | arxiv-cs.LG | 2024-06-14 |
| 100 | AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing in-context learning (ICL) methods for relation extraction (RE) often prioritize language similarity over structural similarity, which can lead to overlooking entity relationships. To address this, we propose an AMR-enhanced retrieval-based ICL method for RE. |
Peitao Han; Lis Kanashiro Pereira; Fei Cheng; Wan Jou She; Eiji Aramaki; | arxiv-cs.CL | 2024-06-14 |
| 101 | SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: 118075 human bounding boxes and 50649 interaction instances are annotated on 11398 keyframes. To benchmark this we propose a two-stage baseline method and conduct extensive experiments to reveal the key factors for a successful human-human interaction detector. |
Tao Wu; Runyu He; Gangshan Wu; Limin Wang; | cvpr | 2024-06-13 |
| 102 | On The Robustness of Document-Level Relation Extraction Models to Entity Name Variations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we systematically investigate the robustness of DocRE models to entity name variations in this work. |
SHIAO MENG et. al. | arxiv-cs.CL | 2024-06-11 |
| 103 | End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This problem prevents the instance retrievers from being optimized for the relation extraction task, and conventionally it must be trained with an objective different from that for relation extraction. To address this issue, we propose a novel End-to-end Trainable Retrieval-Augmented Generation (ETRAG), which allows end-to-end optimization of the entire model, including the retriever, for the relation extraction objective by utilizing a differentiable selection of the $k$ nearest instances. |
Kohei Makino; Makoto Miwa; Yutaka Sasaki; | arxiv-cs.CL | 2024-06-06 |
| 104 | Description Boosting for Zero-Shot Entity and Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we formally define the problem of identifying effective descriptions for zero shot inference. |
GABRIELE PICCO et. al. | arxiv-cs.CL | 2024-06-04 |
| 105 | Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent research efforts have explored the potential of leveraging natural language inference (NLI) techniques to enhance relation extraction (RE). In this vein, we introduce MetaEntail-RE, a novel adaptation method that harnesses NLI principles to enhance RE performance. |
William Hogan; Jingbo Shang; | arxiv-cs.CL | 2024-05-31 |
| 106 | PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Extraction (RE) aims at recognizing the relation between pairs of entities mentioned in a text. Advances in LLMs have had a tremendous impact on NLP. In this work, we … |
Yang Zhou; Shimin Shan; Hongkui Wei; Zhehuan Zhao; Wenshuo Feng; | ArXiv | 2024-05-30 |
| 107 | BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a methodology for enhancing relation extraction from biomedical texts, focusing specifically on chemical-gene interactions. |
Bridget T. McInnes; Jiawei Tang; Darshini Mahendran; Mai H. Nguyen; | arxiv-cs.CL | 2024-05-28 |
| 108 | Knowledge-Driven Cross-Document Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we propose a novel approach, KXDocRE, that embed domain knowledge of entities with input text for cross-document RE. |
Monika Jain; Raghava Mutharaju; Kuldeep Singh; Ramakanth Kavuluru; | arxiv-cs.CL | 2024-05-22 |
| 109 | An Analysis of Sentential Neighbors in Implicit Discourse Relation Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we propose three new methods in which to incorporate context in the task of sentence relation prediction: (1) Direct Neighbors (DNs), (2) Expanded Window Neighbors (EWNs), and (3) Part-Smart Random Neighbors (PSRNs). |
Evi Judge; Reece Suchocki; Konner Syed; | arxiv-cs.CL | 2024-05-15 |
| 110 | DRAM-like Architecture with Asynchronous Refreshing for Continual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches in this field predominantly rely on memory-based methods to alleviate catastrophic forgetting, which overlooks the inherent challenge posed by the varying memory requirements of different relations and the need for a suitable memory refreshing strategy. Drawing inspiration from the mechanisms of Dynamic Random Access Memory (DRAM), our study introduces a novel CRE architecture with an asynchronous refreshing strategy to tackle these challenges. |
TIANCI BU et. al. | www | 2024-05-13 |
| 111 | Multimodal Relation Extraction Via A Mixture of Hierarchical Visual Context Learners Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, research to date has largely ignored the understanding of how hierarchical visual semantics are represented and the characteristics that can benefit relation extraction. To bridge this gap, we propose a novel two-stage hierarchical visual context fusion transformer incorporating the mixture of multimodal experts framework to effectively represent and integrate hierarchical visual features into textual semantic representations. |
XIYANG LIU et. al. | www | 2024-05-13 |
| 112 | OODREB: Benchmarking State-of-the-Art Methods for Out-Of-Distribution Generalization on Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we serve as the first effort to study out-of-distribution (OOD) problems in RE by constructing an out-of-distribution relation extraction benchmark (OODREB) and then investigating the abilities of state-of-the-art (SOTA) RE methods on OODREB in both i.i.d. and OOD settings. |
Haotian Chen; Houjing Guo; Bingsheng Chen; Xiangdong Zhou; | www | 2024-05-13 |
| 113 | TacoERE: Cluster-aware Compression for Event Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing work mainly focuses on directly modeling the entire document, which cannot effectively handle long-range dependencies and information redundancy. To address these issues, we propose a cluster-aware compression method for improving event relation extraction (TacoERE), which explores a compression-then-extraction paradigm. |
YONG GUAN et. al. | arxiv-cs.CL | 2024-05-10 |
| 114 | Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the L3X method which tackles the problem in two stages: (1) recall-oriented generation using a large language model (LLM) with judicious techniques for retrieval augmentation, and (2) precision-oriented scrutinization to validate or prune candidates. |
Sneha Singhania; Simon Razniewski; Gerhard Weikum; | arxiv-cs.CL | 2024-05-04 |
| 115 | Few-shot Biomedical Relation Extraction Using Data Augmentation and Domain Information Related Papers Related Patents Related Grants Related Venues Related Experts View |
Bocheng Guo; Di Zhao; Xin Dong; Jiana Meng; Hongfei Lin; | Neurocomputing | 2024-05-01 |
| 116 | BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains; however, these models encounter issues such as generating inaccurate information or hallucinations. Retrieval-augmented generation provided a solution for these models to update knowledge and enhance their performance. |
Mingchen Li; Halil Kilicoglu; Hua Xu; Rui Zhang; | arxiv-cs.CL | 2024-05-01 |
| 117 | Reading Broadly to Open Your Mind: Improving Open Relation Extraction With Search Documents Under Self-Supervisions Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize distant-supervised annotations to … |
XUMING HU et. al. | IEEE Transactions on Knowledge and Data Engineering | 2024-05-01 |
| 118 | Graphical Reasoning: LLM-based Semi-Open Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a comprehensive exploration of relation extraction utilizing advanced language models, specifically Chain of Thought (CoT) and Graphical Reasoning (GRE) techniques. |
Yicheng Tao; Yiqun Wang; Longju Bai; | arxiv-cs.CL | 2024-04-30 |
| 119 | CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Given the impressive performance of large language models (LLMs) in natural language processing, we propose a new framework called CRE-LLM. |
Zhengpeng Shi; Haoran Luo; | arxiv-cs.CL | 2024-04-28 |
| 120 | Building A Japanese Document-Level Relation Extraction Dataset Assisted By Cross-Lingual Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As an initial attempt, we construct a dataset by transferring an English dataset to Japanese. |
Youmi Ma; An Wang; Naoaki Okazaki; | arxiv-cs.CL | 2024-04-25 |
| 121 | EnzChemRED, A Rich Enzyme Chemistry Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of Natural Language Processing (NLP) methods such as (large) language models that can assist enzyme curation. |
PO-TING LAI et. al. | arxiv-cs.CL | 2024-04-22 |
| 122 | How to Encode Domain Information in Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We explore a multi-domain training setup for RC, and attempt to improve performance by encoding domain information. |
ELISA BASSIGNANA et. al. | arxiv-cs.CL | 2024-04-21 |
| 123 | Retrieval-Augmented Generation-based Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In addressing these challenges, Large Language Models (LLMs) emerge as promising solutions; however, they might return hallucinating responses due to their own training data. To overcome these limitations, Retrieved-Augmented Generation-based Relation Extraction (RAG4RE) in this work is proposed, offering a pathway to enhance the performance of relation extraction tasks. |
Sefika Efeoglu; Adrian Paschke; | arxiv-cs.CL | 2024-04-20 |
| 124 | A Continual Relation Extraction Approach for Knowledge Graph Completeness Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Representing unstructured data in a structured form is most significant for information system management to analyze and interpret it. To do this, the unstructured data might be … |
Sefika Efeoglu; | ArXiv | 2024-04-20 |
| 125 | REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing approaches for cIE suffer from two limitations: (i) they are often pipelines which makes them prone to error propagation, and/or (ii) they are restricted to sentence level which prevents them from capturing long-range dependencies and results in expensive inference time. We address these limitations by proposing REXEL, a highly efficient and accurate model for the joint task of document level cIE (DocIE). |
Nacime Bouziani; Shubhi Tyagi; Joseph Fisher; Jens Lehmann; Andrea Pierleoni; | arxiv-cs.CL | 2024-04-19 |
| 126 | Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE task. To address this limitation, we propose the Variational Multi-Modal Hypergraph Attention Network (VM-HAN) for multi-modal relation extraction. |
QIAN LI et. al. | arxiv-cs.CL | 2024-04-18 |
| 127 | EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, they disregard task and/or dataset-specific constraints, resulting in output structures that lack coherence. In our work, we introduce EnriCo, which mitigates these shortcomings. |
Urchade Zaratiana; Nadi Tomeh; Yann Dauxais; Pierre Holat; Thierry Charnois; | arxiv-cs.CL | 2024-04-18 |
| 128 | GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text. In this paper, we propose a novel approach to this task by formulating it as graph structure learning (GSL). |
Urchade Zaratiana; Nadi Tomeh; Niama El Khbir; Pierre Holat; Thierry Charnois; | arxiv-cs.CL | 2024-04-18 |
| 129 | A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a model that, initialized from LayoutLMv3, can match or outperform the current state-of-the-art results in RE applied to Visually-Rich Documents (VRD) on FUNSD and CORD datasets, without any specific pre-training and with fewer parameters. |
WIAM ADNAN et. al. | arxiv-cs.CL | 2024-04-16 |
| 130 | Causal-Evidence Graph for Causal Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper aims toward an enhancement for automatic causal relation classification from text sources. We introduce a Causal Evidence Graph (CEG), which is a graph-structured … |
Yuni Susanti; Kanji Uchino; | Proceedings of the 39th ACM/SIGAPP Symposium on Applied … | 2024-04-08 |
| 131 | Causality Extraction from Nuclear Licensee Event Reports Using A Hybrid Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposed a hybrid framework for causality detection and extraction from nuclear licensee event reports. |
SHAHIDUR RAHOMAN SOHAG et. al. | arxiv-cs.CL | 2024-04-08 |
| 132 | A Two Dimensional Feature Engineering Method for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a two-dimensional feature engineering method in the 2D sentence representation for relation extraction. |
Hao Wang; Yanping Chen; Weizhe Yang; Yongbin Qin; Ruizhang Huang; | arxiv-cs.CL | 2024-04-07 |
| 133 | Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a meta dataset for few-shot relation extraction, which includes two datasets derived from existing supervised relation extraction datasets NYT29 (Takanobu et al., 2019; Nayak and Ng, 2020) and WIKIDATA (Sorokin and Gurevych, 2017) as well as a few-shot form of the TACRED dataset (Sabo et al., 2021). |
Fahmida Alam; Md Asiful Islam; Robert Vacareanu; Mihai Surdeanu; | arxiv-cs.CL | 2024-04-05 |
| 134 | EGTR: Extracting Graph from Transformer for Scene Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Considering the dependency of the relation extraction task on the object detection task, we propose a novel relation smoothing technique that adjusts the relation label adaptively according to the quality of the detected objects. |
Jinbae Im; JeongYeon Nam; Nokyung Park; Hyungmin Lee; Seunghyun Park; | arxiv-cs.CV | 2024-04-02 |
| 135 | Single-Stage Related Object Detection for Intelligent Industrial Surveillance Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Detecting the position and safe wearing of workers is an significant topic in industrial production. However, mainstream detectors aware object instances individually instead of … |
YANG ZHANG et. al. | IEEE Transactions on Industrial Informatics | 2024-04-01 |
| 136 | Self-Attention Over Tree for Relation Extraction With Data-Efficiency and Computational Efficiency Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Dependency trees parsed from natural language sentences have been proven to be beneficial for the relation extraction task by deep neural networks. However, effectively and … |
Shengfei Lyu; Xiren Zhou; Xingyu Wu; Qiuju Chen; Huanhuan Chen; | IEEE Transactions on Emerging Topics in Computational … | 2024-04-01 |
| 137 | BDCore: Bidirectional Decoding with Co-graph Representation for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Daojian Zeng; Chao Zhao; Dongye Li; Jianhua Dai; | Knowl. Based Syst. | 2024-04-01 |
| 138 | Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to A New Language Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There is a growing interest in the community to build datasets capable of training machine learning models to extract relationships. |
Alistair Plum; Tharindu Ranasinghe; Christoph Purschke; | arxiv-cs.CL | 2024-03-25 |
| 139 | Well-Written Knowledge Graphs: Most Effective RDF Syntaxes for Triple Linearization in End-to-End Extraction of Relations from Texts (Student Abstract) Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Seq-to-seq generative models recently gained attention for solving the relation extraction task. By approaching this problem as an end-to-end task, they surpassed … |
Célian Ringwald; Fabien L. Gandon; Catherine Faron-Zucker; Franck Michel; Hanna Abi Akl; | AAAI Conference on Artificial Intelligence | 2024-03-24 |
| 140 | Beyond Entities: A Large-Scale Multi-Modal Knowledge Graph with Triplet Fact Grounding Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Much effort has been devoted to building multi-modal knowledge graphs by visualizing entities on images, but ignoring the multi-modal information of the relation between entities. … |
JINGPING LIU et. al. | AAAI Conference on Artificial Intelligence | 2024-03-24 |
| 141 | MixRED: A Mix-lingual Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Due to the lack of a dedicated dataset, the effectiveness of existing relation extraction models in such a scenario is largely unexplored. To address this issue, we introduce a novel task of considering relation extraction in the mix-lingual scenario called MixRE and constructing the human-annotated dataset MixRED to support this task. |
LINGXING KONG et. al. | arxiv-cs.AI | 2024-03-22 |
| 142 | CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Centered around the outsourcing practices of companies within fund prospectuses in Germany, we introduce a dataset specifically designed for named entity recognition and relation extraction tasks. |
Neda Foroutan; Markus Schröder; Andreas Dengel; | arxiv-cs.CL | 2024-03-22 |
| 143 | Event Temporal Relation Extraction Based on Retrieval-Augmented on LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel retrieval-augmented TempRel extraction approach, leveraging knowledge retrieved from large language models (LLMs) to enhance prompt templates and verbalizers. |
Xiaobin Zhang; Liangjun Zang; Qianwen Liu; Shuchong Wei; Songlin Hu; | arxiv-cs.CL | 2024-03-22 |
| 144 | CHisIEC: An Information Extraction Corpus for Ancient Chinese History Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In our commitment to expediting ancient history and culture, we present the “Chinese Historical Information Extraction Corpus”(CHisIEC). |
Xuemei Tang; Zekun Deng; Qi Su; Hao Yang; Jun Wang; | arxiv-cs.CL | 2024-03-22 |
| 145 | AutoRE: Document-Level Relation Extraction with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, certain approaches resort to treating relations as candidate choices integrated into prompt templates, leading to inefficient processing and suboptimal performance when tackling Document-Level Relation Extraction (DocRE) tasks, which entail handling multiple relations and triplet facts distributed across a given document, posing distinct challenges. To overcome these limitations, we introduce AutoRE, an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts). |
Lilong Xue; Dan Zhang; Yuxiao Dong; Jie Tang; | arxiv-cs.CL | 2024-03-21 |
| 146 | GraphERE: Jointly Multiple Event-Event Relation Extraction Via Graph-Enhanced Event Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The interconnection between relations (e.g., temporal and causal relations usually interact with each other ) is ignored. To solve the above problems, this paper proposes a jointly multiple ERE framework called GraphERE based on Graph-enhanced Event Embeddings. |
Haochen Li; Di Geng; | arxiv-cs.CL | 2024-03-19 |
| 147 | Pipelined Biomedical Event Extraction Rivaling Joint Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional biomedical event extraction usually adopts a pipelined approach, which contains trigger identification, argument role recognition, and finally event construction either using specific rules or by machine learning. In this paper, we propose an n-ary relation extraction method based on the BERT pre-training model to construct Binding events, in order to capture the semantic information about an event’s context and its participants. |
Pengchao Wu; Xuefeng Li; Jinghang Gu; Longhua Qian; Guodong Zhou; | arxiv-cs.CL | 2024-03-18 |
| 148 | Information Extraction: An Application to The Domain of Hyper-local Financial Data on Developing Countries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the need for financial data on company activities in developing countries for development research and economic analysis, such data does not exist. In this project, we develop and evaluate two Natural Language Processing (NLP) based techniques to address this issue. |
Abuzar Royesh; Olamide Oladeji; | arxiv-cs.CL | 2024-03-13 |
| 149 | Advancing Chinese Biomedical Text Mining with Community Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Objective: This study aims to review the recent advances in community challenges for biomedical text mining in China. |
HUI ZONG et. al. | arxiv-cs.AI | 2024-03-07 |
| 150 | Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. |
Robert Vacareanu; Fahmida Alam; Md Asiful Islam; Haris Riaz; Mihai Surdeanu; | arxiv-cs.CL | 2024-03-05 |
| 151 | DP-CRE: Continual Relation Extraction Via Decoupled Contrastive Learning and Memory Structure Preservation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To handle the problem, we introduce the DecouPled CRE (DP-CRE) framework that decouples the process of prior information preservation and new knowledge acquisition. |
Mengyi Huang; Meng Xiao; Ludi Wang; Yi Du; | arxiv-cs.CL | 2024-03-05 |
| 152 | FCDS: Fusing Constituency and Dependency Syntax Into Document-Level Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Document-level Relation Extraction (DocRE) aims to identify relation labels between entities within a single document. It requires handling several sentences and reasoning over … |
Xudong Zhu; Zhao Kang; Bei Hui; | ArXiv | 2024-03-04 |
| 153 | KnowCTI: Knowledge-based Cyber Threat Intelligence Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
GAOSHENG WANG et. al. | Comput. Secur. | 2024-03-01 |
| 154 | Few-Shot Relation Extraction with Hybrid Visual Evidence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a multi-modal few-shot relation extraction model (MFS-HVE) that leverages both textual and visual semantic information to learn a multi-modal representation jointly. |
Jiaying Gong; Hoda Eldardiry; | arxiv-cs.CL | 2024-03-01 |
| 155 | Pointing Out The Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we describe several semantically-motivated strategies to generate adversarial examples by replacing entity mentions and investigate how state-of-the-art RE models perform under pressure. |
Gennaro Nolano; Moritz Blum; Basil Ell; Philipp Cimiano; | arxiv-cs.CL | 2024-02-29 |
| 156 | On The Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new framework, Clean-LaVe, which aims to utilize silver standard data to enhance the zero-shot performance. |
Jianwei Wang; Tianyin Wang; Ziqian Zeng; | arxiv-cs.CL | 2024-02-28 |
| 157 | Joint Data Augmentation and Knowledge Distillation for Few-shot Continual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhongcheng Wei; Yunping Zhang; Bin Lian; Yongjian Fan; Jijun Zhao; | Appl. Intell. | 2024-02-28 |
| 158 | Interactive Optimization of Relation Extraction Via Knowledge Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View |
YUHUA LIU et. al. | J. Vis. | 2024-02-26 |
| 159 | Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper harnesses prompt learning to explore the implicit capabilities of pre-trained language models to address the above two challenges, thereby making language models better continual few-shot relation extractors. Specifically, we propose a Contrastive Prompt Learning framework, which designs prompt representation to acquire more generalized knowledge that can be easily adapted to old and new categories, and margin-based contrastive learning to focus more on hard samples, therefore alleviating catastrophic forgetting and overfitting issues. |
Shengkun Ma; Jiale Han; Yi Liang; Bo Cheng; | arxiv-cs.CL | 2024-02-23 |
| 160 | Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents our effort in the data acquisition, annotation methodology, and thorough analysis of the annotated dataset. |
Mohan Raj Chanthran; Lay-Ki Soon; Huey Fang Ong; Bhawani Selvaretnam; | arxiv-cs.CL | 2024-02-22 |
| 161 | Small Language Models As Effective Guides for Large Language Models in Chinese Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: An important problem in the field of RE is long-tailed data, while not much attention is paid to this problem using LLM approaches. Therefore, in this paper, we propose SLCoLM, a model collaboration framework, to mitigate the data long-tail problem. |
Xuemei Tang; Jun Wang; | arxiv-cs.CL | 2024-02-22 |
| 162 | Combining Language and Graph Models for Semi-structured Information Extraction on The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present GraphScholarBERT, an open-domain information extraction method based on a joint graph and language model structure. |
Zhi Hong; Kyle Chard; Ian Foster; | arxiv-cs.IR | 2024-02-21 |
| 163 | Zero-shot Generalization Across Architectures for Visual Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Using a minimalist vision dataset and a measure of generalizability, we show that popular networks, from deep convolutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and across architectures. |
Evan Gerritz; Luciano Dyballa; Steven W. Zucker; | arxiv-cs.CV | 2024-02-21 |
| 164 | APRE: Annotation-Aware Prompt-Tuning for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
CHAO WEI et. al. | Neural Process. Lett. | 2024-02-21 |
| 165 | Reliable Data Generation and Selection for Low-Resource Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Self-RDGS, a method for Self-supervised Reliable Data Generation and Selection in low-resource RE tasks. |
Junjie Yu; Xing Wang; Wenliang Chen; | aaai | 2024-02-20 |
| 166 | A Hierarchical Network for Multimodal Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we leverage video information to provide additional evidence for understanding long dependencies and offer a wider perspective for identifying relevant mentions, thus giving rise to a new task named Multimodal Document-level Relation Extraction (MDocRE). |
LINGXING KONG et. al. | aaai | 2024-02-20 |
| 167 | How Important Is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Cutting edge techniques developed in the general NLP domain are often subsequently applied to the high-value, data-rich biomedical domain. |
Aviv Brokman; Ramakanth Kavuluru; | arxiv-cs.CL | 2024-02-20 |
| 168 | Continual Relation Extraction Via Sequential Multi-Task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Continual Relation Extraction via Sequential Multi-task Learning (CREST), a novel CRE approach built upon a tailored Multi-task Learning framework for continual learning. |
Thanh-Thien Le; Manh Nguyen; Tung Thanh Nguyen; Linh Ngo Van; Thien Huu Nguyen; | aaai | 2024-02-20 |
| 169 | Open-Vocabulary Video Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, many current video understanding tasks prioritize general action classification and overlook the actors and relationships that shape the nature of the action, resulting in a superficial understanding of the action. Motivated by this, we introduce Open-vocabulary Video Relation Extraction (OVRE), a novel task that views action understanding through the lens of action-centric relation triplets. |
Wentao Tian; Zheng Wang; Yuqian Fu; Jingjing Chen; Lechao Cheng; | aaai | 2024-02-20 |
| 170 | Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We evaluate our approach on benchmark datasets – DocRED, ReDocRED, and DWIE. |
Monika Jain; Raghava Mutharaju; Ramakanth Kavuluru; Kuldeep Singh; | aaai | 2024-02-20 |
| 171 | An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel method for joint entity and relation extraction from unstructured text by framing it as a conditional sequence generation problem. |
Urchade Zaratiana; Nadi Tomeh; Pierre Holat; Thierry Charnois; | aaai | 2024-02-20 |
| 172 | A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, incomplete labeling in document-level RE has received increasing attention, and some studies have used methods such as positive-unlabeled learning to tackle this issue, but there is still a lot of room for improvement. Motivated by this, we propose a positive-augmentation and positive-mixup positive-unlabeled metric learning framework (P3M). |
Ye Wang; Huazheng Pan; Tao Zhang; Wen Wu; Wenxin Hu; | aaai | 2024-02-20 |
| 173 | Creating A Fine Grained Entity Type Taxonomy Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigate the potential of GPT-4 and its advanced iteration, GPT-4 Turbo, in autonomously developing a detailed entity type taxonomy. |
Michael Gunn; Dohyun Park; Nidhish Kamath; | arxiv-cs.CL | 2024-02-19 |
| 174 | Constraint Information Extraction for 3D Geological Modelling Using A Span-based Joint Entity and Relation Extraction Model Related Papers Related Patents Related Grants Related Venues Related Experts View |
CAN ZHUANG et. al. | Earth Sci. Informatics | 2024-02-16 |
| 175 | RSRNeT: A Novel Multi-modal Network Framework for Named Entity Recognition and Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Named entity recognition (NER) and relation extraction (RE) are two important technologies employed in knowledge extraction for constructing knowledge graphs. Uni-modal NER and RE … |
Min Wang; Hongbin Chen; Dingcai Shen; Baolei Li; Shiyu Hu; | PeerJ Comput. Sci. | 2024-02-09 |
| 176 | Prompting Implicit Discourse Relation Annotation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work investigates several proven prompting techniques to improve ChatGPT’s recognition of discourse relations. |
Frances Yung; Mansoor Ahmad; Merel Scholman; Vera Demberg; | arxiv-cs.CL | 2024-02-07 |
| 177 | Drug–Drug Interaction Relation Extraction Based on Deep Learning: A Review Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Drug–drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects … |
Mingliang Dou; Jijun Tang; Prayag Tiwari; Yijie Ding; Fei Guo; | ACM Computing Surveys | 2024-02-07 |
| 178 | Leveraging Semantic Text Analysis to Improve The Performance of Transformer-Based Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, … |
Marie-Therese Charlotte Evans; Majid Latifi; M. Ahsan; J. Haider; | Inf. | 2024-02-06 |
| 179 | PTCAS: Prompt Tuning with Continuous Answer Search for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yang Chen; Bowen Shi; Ke Xu; | Inf. Sci. | 2024-02-01 |
| 180 | Temporal Relation Extraction with Contrastive Prototypical Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View |
Chenhan Yuan; Qianqian Xie; Sophia Ananiadou; | Knowl. Based Syst. | 2024-02-01 |
| 181 | Document-level Relation Extraction with Global and Path Dependencies Related Papers Related Patents Related Grants Related Venues Related Experts View |
Wei Jia; Ruizhe Ma; Li Yan; Weinan Niu; Z. Ma; | Knowl. Based Syst. | 2024-02-01 |
| 182 | GAP: A Novel Generative Context-Aware Prompt-tuning Method for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhenbin Chen; Zhixin Li; Yufei Zeng; Canlong Zhang; Huifang Ma; | Expert Syst. Appl. | 2024-02-01 |
| 183 | Evidence Reasoning and Curriculum Learning for Document-Level Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level Relation Extraction (RE) is a promising task aiming at identifying relations of multiple entity pairs in a document. Compared with the sentence-level counterpart, … |
TIANYU XU et. al. | IEEE Transactions on Knowledge and Data Engineering | 2024-02-01 |
| 184 | MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder-decoder framework, to jointly extract entities and relations from material science articles as a triplet ($entity1, relation, entity2$). |
ANKAN MULLICK et. al. | arxiv-cs.CL | 2024-01-18 |
| 185 | Distantly Supervised Morpho-Syntactic Model for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a method for the extraction and categorisation of an unrestricted set of relationships from text. |
Nicolas Gutehrlé; Iana Atanassova; | arxiv-cs.CL | 2024-01-18 |
| 186 | Dynamic Relation Transformer for Contextual Text Block Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new framework that frames CTBD as a graph generation problem. |
JIAWEI WANG et. al. | arxiv-cs.CV | 2024-01-17 |
| 187 | BERTologyNavigator: Advanced Question Answering with BERT-based Semantics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce the BERTologyNavigator — a two-phased system that combines relation extraction techniques and BERT embeddings to navigate the relationships within the DBLP Knowledge Graph (KG). |
Shreya Rajpal; Ricardo Usbeck; | arxiv-cs.CL | 2024-01-17 |
| 188 | EMBRE: Entity-aware Masking for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Entity-aware Masking for Biomedical Relation Extraction (EMBRE) method for biomedical relation extraction, as applied in the context of the BioRED challenge Task 1, in which human-annotated entities are provided as input. |
Mingjie Li; Karin Verspoor; | arxiv-cs.CL | 2024-01-15 |
| 189 | Joint Extraction of Uyghur Medicine Knowledge with Edge Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This mode faces challenges such as error propagation between tasks, insufficient consideration of dependencies between the two subtasks, and the neglect of interrelations between different relations within a sentence. To address these challenges, a joint extraction model with parameter sharing in edge computing is proposed, named CoEx-Bert. |
Fan Lu; Quan Qi; Huaibin Qin; | arxiv-cs.CL | 2024-01-13 |
| 190 | Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. |
Jiaxin Wang; Lingling Zhang; Jun Liu; Tianlin Guo; Wenjun Wu; | arxiv-cs.CL | 2024-01-11 |
| 191 | Knowledge Graph Extraction of Business Interactions from News Text for Business Networking Analysis Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Network representation of data is key to a variety of fields and their applications including trading and business. A major source of data that can be used to build insightful … |
Didier Gohourou; Kazuhiro Kuwabara; | Mach. Learn. Knowl. Extr. | 2024-01-07 |
| 192 | SREMIC: Spatial Relation Extraction-based Malware Image Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Around 800,000 people fall prey to cyberattacks annually, most often by “malware”. Malware has the potential to become a destructive weapon in Cyber-world. It is a difficult task … |
INZAMAMUL ALAM et. al. | 2024 18th International Conference on Ubiquitous … | 2024-01-03 |
| 193 | Enhancing Relation Extraction from Biomedical Texts By Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View |
Masaki Asada; Ken Fukuda; | Interacción | 2024-01-01 |
| 194 | A Novel Joint Training Model for Knowledge Base Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In knowledge base question answering (KBQA) systems, relation detection and entity recognition are two core components. However, since the relation detection in KBQA contains … |
Shouhui Wang; Biao Qin; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2024-01-01 |
| 195 | LAILab at Chemotimelines 2024: Finetuning Sequence-to-sequence Language Models for Temporal Relation Extraction Towards Cancer Patient Undergoing Chemotherapy Treatment Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we report our effort to tackle the challenge of extracting chemotimelines from EHR notes across a dataset of three cancer types. We focus on the two subtasks: 1) … |
Shohreh Haddadan; Tuan-Dung Le; Thanh Duong; Thanh Thieu; | Clinical Natural Language Processing Workshop | 2024-01-01 |
| 196 | Episodic Memory Retrieval from LLMs: A Neuromorphic Mechanism to Generate Commonsense Counterfactuals for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Large language models (LLMs) have achieved satisfactory performance in counterfactual generation. However, confined by the stochastic generation process of LLMs, there often are … |
Xin Miao; Yongqi Li; Shen Zhou; Tieyun Qian; | Annual Meeting of the Association for Computational … | 2024-01-01 |
| 197 | Geospatial Topological Relation Extraction from Text with Knowledge Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View |
WEI HU et. al. | SDM | 2024-01-01 |
| 198 | UTSA-NLP at ChemoTimelines 2024: Evaluating Instruction-Tuned Language Models for Temporal Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: This paper presents our approach for the 2024 ChemoTimelines shared task. Specifically, we explored using Large Language Models (LLMs) for temporal relation extraction. We … |
Xingmeng Zhao; A. Rios; | Clinical Natural Language Processing Workshop | 2024-01-01 |
| 199 | A Few-Shot Learning-Focused Survey on Recent Named Entity Recognition and Relation Classification Models Related Papers Related Patents Related Grants Related Venues Related Experts View |
S. Alqaaidi; Elika Bozorgi; Afsaneh Shams; Krzysztof J. Kochut; | International Conference on Data Technologies and … | 2024-01-01 |
| 200 | One General Teacher for Multi-Data Multi-Task: A New Knowledge Distillation Framework for Discourse Relation Analysis Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Automatically identifying the discourse relations can help many downstream NLP tasks such as reading comprehension and machine translation. It can be categorized into explicit and … |
Congcong Jiang; Tieyun Qian; Bing Liu; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2024-01-01 |
| 201 | RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In order to enable extraction of structured clinical data from unstructured radiology reports, we introduce RadGraph-XL, a large-scale, expert-annotated dataset for clinical … |
JEAN-BENOIT DELBROUCK et. al. | Annual Meeting of the Association for Computational … | 2024-01-01 |
| 202 | Generalizing Across Languages and Domains for Discourse Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The availability of corpora annotated for discourse relations is limited and discourse relation classification performance varies greatly depending on both language and domain. … |
Peter Bourgonje; Vera Demberg; | SIGDIAL Conferences | 2024-01-01 |
| 203 | Entity-Relation Extraction As Full Shallow Semantic Dependency Parsing Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Entity-relation extraction is the essential information extraction task and can be decomposed into Named Entity Recognition (NER) and Relation Extraction (RE) subtasks. This paper … |
Shu Jiang; Z. Li; Hai Zhao; Weiping Ding; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2024-01-01 |
| 204 | Document-level Denoising Relation Extraction with False-negative Mining and Reinforced Positive-class Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View |
Daojian Zeng; Jianling Zhu; Hongting Chen; Jianhua Dai; Lincheng Jiang; | Inf. Process. Manag. | 2024-01-01 |
| 205 | A Hierarchical Convolutional Model for Biomedical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ying Hu; Yanping Chen; Ruizhang Huang; Yongbin Qin; Qinghua Zheng; | Inf. Process. Manag. | 2024-01-01 |
| 206 | Towards Bridged Vision and Language: Learning Cross-Modal Knowledge Representation for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In natural language processing, relation extraction (RE) is to detect and classify the semantic relationship of two given entities within a sentence. Previous RE methods consider … |
JUNHAO FENG et. al. | IEEE Transactions on Circuits and Systems for Video … | 2024-01-01 |
| 207 | Enhancing Low-Resource Relation Representations Through Multi-View Decoupling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we highlight the importance of learning high-quality relation representation in low-resource scenarios for RE, and propose a novel prompt-based relation representation method, named MVRE (\underline{M}ulti-\underline{V}iew \underline{R}elation \underline{E}xtraction), to better leverage the capacity of PLMs to improve the performance of RE within the low-resource prompt-tuning paradigm. |
CHENGHAO FAN et. al. | arxiv-cs.CL | 2023-12-26 |
| 208 | Rationale-Enhanced Language Models Are Better Continual Relation Learners Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the issue, we introduce rationale, i. e. , the explanations of relation classification results generated by Large Language Models (LLM), into CRE task. |
Weimin Xiong; Yifan Song; Peiyi Wang; Sujian Li; | emnlp | 2023-12-22 |
| 209 | Improving Unsupervised Relation Extraction By Augmenting Diverse Sentence Pairs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose AugURE with both within-sentence pairs augmentation and augmentation through cross-sentence pairs extraction to increase the diversity of positive pairs and strengthen the discriminative power of contrastive learning. |
Qing Wang; Kang Zhou; Qiao Qiao; Yuepei Li; Qi Li; | emnlp | 2023-12-22 |
| 210 | Anaphor Assisted Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such an approach has difficulties in learning fine-grained interactions between entities across different sentences, resulting in sub-optimal performance. To address these issues, we propose an Anaphor-Assisted (AA) framework for DocRE tasks. |
CHONGGANG LU et. al. | emnlp | 2023-12-22 |
| 211 | RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. |
SHIAO MENG et. al. | emnlp | 2023-12-22 |
| 212 | HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By investigating the class separation of an FSRE model, we find that model upper layers are prone to learn relation-specific knowledge. Therefore, in this paper, we propose a HyperNetwork-based Decoupling approach to improve the generalization of FSRE models. |
LIANG ZHANG et. al. | emnlp | 2023-12-22 |
| 213 | Open-world Semi-supervised Generalized Relation Discovery Aligned in A Real-world Setting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, we observe that popular relations can often be implicitly inferred through specific patterns, while long-tail relations tend to be explicitly expressed. Motivated by these insights, we present a method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data. |
William Hogan; Jiacheng Li; Jingbo Shang; | emnlp | 2023-12-22 |
| 214 | TIMELINE: Exhaustive Annotation of Temporal Relations Supporting The Automatic Ordering of Events in News Articles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Temporal relation extraction models have thus far been hindered by a number of issues in existing temporal relation-annotated news datasets, including: (1) low inter-annotator agreement due to the lack of specificity of their annotation guidelines in terms of what counts as a temporal relation; (2) the exclusion of long-distance relations within a given document (those spanning across different paragraphs); and (3) the exclusion of events that are not centred on verbs. This paper aims to alleviate these issues by presenting a new annotation scheme that clearly defines the criteria based on which temporal relations should be annotated. |
Sarah Alsayyahi; Riza Batista-Navarro; | emnlp | 2023-12-22 |
| 215 | GPT-RE: In-context Learning for Relation Extraction Using Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose GPT-RE to successfully address the aforementioned issues by (1) incorporating task-aware representations in demonstration retrieval; and (2) enriching the demonstrations with gold label-induced reasoning logic. |
ZHEN WAN et. al. | emnlp | 2023-12-22 |
| 216 | Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose HyperGraph neural network for ERE (HGERE), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model). |
Zhaohui Yan; Songlin Yang; Wei Liu; Kewei Tu; | emnlp | 2023-12-22 |
| 217 | CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. |
Philipp Borchert; Jochen De Weerdt; Kristof Coussement; Arno De Caigny; Marie-Francine Moens; | emnlp | 2023-12-22 |
| 218 | EDeR: Towards Understanding Dependency Relations Between Events Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such relationships consider two events to be independent in terms of syntax and semantics, but they fail to recognize the interdependence between events. To bridge this gap, we introduce a human-annotated Event Dependency Relation dataset (EDeR). |
Ruiqi Li; Patrik Haslum; Leyang Cui; | emnlp | 2023-12-22 |
| 219 | KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the flexibility of language expression and the lack of high-quality Chinese annotation datasets, it is still a challenge to accurately identify such relations from Chinese unstructured texts. To tackle this problem, we propose a Knowledge Enhanced Prompt Learning (KEPL) method for Chinese hypernym-hyponym relation extraction. |
Ningchen Ma; Dong Wang; Hongyun Bao; Lei He; Suncong Zheng; | emnlp | 2023-12-22 |
| 220 | Document-level Relationship Extraction By Bidirectional Constraints of Beta Rules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we proposes Bidirectional Constraints of Beta Rules(BCBR), a novel logic constraint framework. |
YICHUN LIU et. al. | emnlp | 2023-12-22 |
| 221 | Generating Commonsense Counterfactuals for Stable Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, to identify causal terms accurately, we introduce an intervention-based strategy and leverage a constituency parser for correction. |
Xin Miao; Yongqi Li; Tieyun Qian; | emnlp | 2023-12-22 |
| 222 | Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, vanilla in-context learning is infeasible for DocRE due to the plenty of predefined fine-grained relation types and the uncontrolled generations of LLMs. To tackle this issue, we propose a method integrating an LLM and a natural language inference (NLI) module to generate relation triples, thereby augmenting document-level relation datasets. |
Junpeng Li; Zixia Jia; Zilong Zheng; | emnlp | 2023-12-22 |
| 223 | Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, how to effectively harness massive instance-label pairs to encompass the learned representation with semantic richness in this learning paradigm is not fully explored. To address this gap, we introduce a novel synergistic anchored contrastive pre-training framework. |
DA LUO et. al. | arxiv-cs.CL | 2023-12-19 |
| 224 | Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our hypothesis in this paper is that relation extraction models can be improved by capturing relationships in a more direct way. |
Frank Mtumbuka; Steven Schockaert; | arxiv-cs.CL | 2023-12-18 |
| 225 | HyperPIE: Hyperparameter Information Extraction from Scientific Publications Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we formalize and tackle hyperparameter information extraction (HyperPIE) as an entity recognition and relation extraction task. |
Tarek Saier; Mayumi Ohta; Takuto Asakura; Michael Färber; | arxiv-cs.CL | 2023-12-17 |
| 226 | Relation Extraction Based on Dual-Path Graph Convolutional Networks Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Graph convolutional networks (GCNs) have found widespread application in relation extraction. In current research, the majority of GCNs employ shallow network architectures due to … |
Junkai Wang; Jianbin Wu; Lixin Zhou; Qian Zhang; Xuanyu Zhang; | 2023 IEEE International Conferences on Internet of Things … | 2023-12-17 |
| 227 | MORE: A Multimodal Object-Entity Relation Extraction Dataset with A Benchmark Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new task, namely Multimodal Object-Entity Relation Extraction, which aims to extract object-entity relational facts from image and text data. |
LIANG HE et. al. | arxiv-cs.MM | 2023-12-15 |
| 228 | TreeBERT: Advanced Representation Learning for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As the COVID-19 pandemic has subsided, it remains crucial to analyze the vast amount of research produced during this period to advance our understanding of effective medical … |
Shashank Hebbar; Ying Xie; Jiang Zhong; | 2023 IEEE International Conference on Big Data (BigData) | 2023-12-15 |
| 229 | High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered By Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Methods: We formulate the relation extraction task as binary classifications for large language models. |
Songchi Zhou; Sheng Yu; | arxiv-cs.CL | 2023-12-13 |
| 230 | BED: Bi-Encoder-Decoder Model for Canonical Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, they are incapable of representing novel entities, since no embeddings have been learned for them. In this paper, we propose a novel framework, Bi-Encoder-Decoder (BED), to solve the above issues. |
Nantao Zheng; Siyu Long; Xinyu Dai; | arxiv-cs.CL | 2023-12-12 |
| 231 | Knowledge-Based Intelligent Text Simplification for Biological Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction from biological publications plays a pivotal role in accelerating scientific discovery and advancing medical research. While vast amounts of this knowledge is … |
Jaskaran Gill; Madhu Chetty; Suryani Lim; Jennifer Hallinan; | Informatics | 2023-12-11 |
| 232 | Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The task of multimodal relation extraction has attracted significant research attention, but progress is constrained by the scarcity of available training data. One natural … |
Zilin Du; Haoxin Li; Xu Guo; Boyang Li; | ArXiv | 2023-12-05 |
| 233 | Joint Biomedical Entity and Relation Extraction with Unified Interaction Maps Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Automatic extraction of entities and their relations from unstructured literature to form structured triples is essential for biomedical knowledge construction. Although most … |
HAIXIN TAN et. al. | 2023 IEEE International Conference on Bioinformatics and … | 2023-12-05 |
| 234 | Entity Relation Aware Graph Neural Ranking for Biomedical Information Retrieval Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The performance of biomedical information retrieval greatly depends on biomedical knowledge; however the knowledge of available medical knowledge base is often incomplete and … |
Yichen He; Xiaofeng Liu; Jinlong Hu; Shoubin Dong; | 2023 IEEE International Conference on Bioinformatics and … | 2023-12-05 |
| 235 | Topic-BiGRU-U-Net for Document-level Relation Extraction from Biomedical Literature Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level biomedical relation extraction refers to extract relationship facts from unstructured biomedical literature. Due to the fact that many relationship facts span … |
Yali Zhao; Rong Yan; | 2023 IEEE International Conference on Bioinformatics and … | 2023-12-05 |
| 236 | ProBioRE: A Framework for Biomedical Causal Relation Extraction Based on Dual-head Prompt and Prototypical Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting relationships among events typically aims to recognize the precise relation between two given events. For the task of event causal relation extraction in the biomedical … |
Lishuang Li; Wanting Ning; | 2023 IEEE International Conference on Bioinformatics and … | 2023-12-05 |
| 237 | Towards Deep Understanding of Graph Convolutional Networks for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
TAO WU et. al. | Data Knowl. Eng. | 2023-12-01 |
| 238 | Document-level Relation Extraction with Three Channels Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhanjun Zhang; Shan Zhao; Haoyu Zhang; Qian Wan; Jie Liu; | Knowl. Based Syst. | 2023-12-01 |
| 239 | Relational Concept Enhanced Prototypical Network for Incremental Few-shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
RONG MA et. al. | Knowl. Based Syst. | 2023-12-01 |
| 240 | A Recollect-tuning Method for Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yizhao Wu; Yanping Chen; Yongbin Qin; Ruixue Tang; Qinghua Zheng; | Expert Syst. Appl. | 2023-12-01 |
| 241 | How Well ChatGPT Understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we assess ChatGPT’s capability in extracting entities and relations from the Malaysian English News (MEN) dataset. |
Mohan Raj Chanthran; Lay-Ki Soon; Huey Fang Ong; Bhawani Selvaretnam; | arxiv-cs.CL | 2023-11-20 |
| 242 | Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The development of language models has been influencing approaches to relation extraction (RE) problems. Although large language models (LLMs) have demonstrated breakthrough … |
Hangtian Zhao; Hankiz Yilahun; A. Hamdulla; | 2023 International Conference on Asian Language Processing … | 2023-11-18 |
| 243 | GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The advent of Large Language Models (LLMs) heralds a paradigm shift, suggesting the feasibility of a singular model addressing multiple IE subtasks. In this vein, we introduce the General Information Extraction Large Language Model (GIELLM), which integrates text Classification, Sentiment Analysis, Named Entity Recognition, Relation Extraction, and Event Extraction using a uniform input-output schema. |
Chengguang Gan; Qinghao Zhang; Tatsunori Mori; | arxiv-cs.CL | 2023-11-12 |
| 244 | Relation Extraction in Underexplored Biomedical Domains: A Diversity-optimised Sampling and Synthetic Data Generation Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We provide our best performing (f1-score=59.0) BioGPT-Large model for end-to-end RE of natural-products relationships along with all the generated synthetic data and the evaluation dataset. |
Maxime Delmas; Magdalena Wysocka; André Freitas; | arxiv-cs.CL | 2023-11-10 |
| 245 | Relation Extraction in Underexplored Biomedical Domains: A Diversity-optimized Sampling and Synthetic Data Generation Approach Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The sparsity of labeled data is an obstacle to the development of Relation Extraction (RE) models and the completion of databases in various biomedical areas. While being of high … |
Maxime Delmas; Magdalena Wysocka; André Freitas; | Computational Linguistics | 2023-11-10 |
| 246 | Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach for few-shot relation extraction using large language models, named CoT-ER, chain-of-thought with explicit evidence reasoning. |
Xilai Ma; Jing Li; Min Zhang; | arxiv-cs.CL | 2023-11-10 |
| 247 | Improving Vision-and-Language Reasoning Via Spatial Relations Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most importantly, the spatial distribution of the visual objects is basically neglected. To address the above issue, we propose to construct the spatial relation graph based on the given visual scenario. |
CHENG YANG et. al. | arxiv-cs.CV | 2023-11-09 |
| 248 | Relation Extraction Model Based on Semantic Enhancement Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For the contribution of the possible subject, finally combine the relationship pre-classification results to weight the enhanced semantics of each relationship to find the enhanced semantics of the possible subject, and send the enhanced semantics combined with the possible subject to the object and relationship extraction module. |
Peiyu Liu; Junping Du; Yingxia Shao; Zeli Guan; | arxiv-cs.CL | 2023-11-05 |
| 249 | Joint Relational Triple Extraction Based on Potential Relation Detection and Conditional Entity Mapping Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiong Zhou; Qinghua Zhang; Man Gao; Guoyin Wang; | Appl. Intell. | 2023-11-03 |
| 250 | Let’s Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus and affected by the characteristics of the input text.To address these limitations, we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural knowledge base for API relation inference. |
QING HUANG et. al. | arxiv-cs.SE | 2023-11-02 |
| 251 | Multi-transformer Based on Prototypical Enhancement Network for Few-shot Relation Classification with Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hongfei Du; Qinghua Zhang; Keyuan Li; Fan Zhao; Guoyin Wang; | Neurocomputing | 2023-11-01 |
| 252 | U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Within Open Relation Extraction (ORE) tasks, the Zero-shot ORE method is to generalize undefined relations from predefined relations, while the Unsupervised ORE method is to … |
JIE ZHOU et. al. | Transactions of the Association for Computational … | 2023-11-01 |
| 253 | A Neural Expectation-Maximization Framework for Noisy Multi-Label Text Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multi-label text classification (MLTC) has a wide range of real-world applications. Neural networks recently promoted the performance of MLTC models. Training these neural-network … |
J. Chen; Richong Zhang; J. Xu; Chunming Hu; Yongyi Mao; | IEEE Transactions on Knowledge and Data Engineering | 2023-11-01 |
| 254 | Discourse Relations Classification and Cross-Framework Discourse Relation Classification Through The Lens of Cognitive Dimensions: An Empirical Investigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that discourse relations can be effectively captured by some simple cognitively inspired dimensions proposed by Sanders et al.(2018). |
Yingxue Fu; | arxiv-cs.CL | 2023-11-01 |
| 255 | Deep Purified Feature Mining Model for Joint Named Entity Recognition and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
YOUWEI WANG et. al. | Inf. Process. Manag. | 2023-11-01 |
| 256 | Contextual Reinforcement, Entity Delimitation and Generative Data Augmentation for Entity Recognition and Relation Extraction in Official Documents Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Transformer architectures have become the main component of various state-of-the-art methods for natural language processing tasks, such as Named Entity Recognition and Relation … |
F. BELÉM et. al. | J. Inf. Data Manag. | 2023-10-31 |
| 257 | Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. |
Fei Cheng; Masayuki Asahara; Ichiro Kobayashi; Sadao Kurohashi; | arxiv-cs.CL | 2023-10-31 |
| 258 | Relation-driven Query of Multiple Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We proposed RelaQ, an interactive system that supports the time series query via relation specifications. |
SHUHAN LIU et. al. | arxiv-cs.HC | 2023-10-30 |
| 259 | A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance. |
Sakher Khalil Alqaaidi; Elika Bozorgi; Afsaneh Shams; Krzysztof Kochut; | arxiv-cs.CL | 2023-10-29 |
| 260 | Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data. |
HAO ZHANG et. al. | arxiv-cs.LG | 2023-10-29 |
| 261 | Nearest Neighbor Search Over Vectorized Lexico-Syntactic Patterns for Relation Extraction from Financial Documents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Apart from this, Real world financial documents such as various 10-X reports (including 10-K, 10-Q, etc.) of publicly traded companies pose another challenge to rule-based systems in terms of longer and complex sentences. In this paper, we introduce a simple approach that consults training relations at test time through a nearest-neighbor search over dense vectors of lexico-syntactic patterns and provides a simple yet effective means to tackle the above issues. |
Pawan Kumar Rajpoot; Ankur Parikh; | arxiv-cs.CL | 2023-10-26 |
| 262 | Prompt Me Up: Unleashing The Power of Alignments for Multimodal Entity and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite attempts at various fusions, previous works have overlooked many unlabeled image-caption pairs, such as NewsCLIPing. This paper proposes innovative pre-training objectives for entity-object and relation-image alignment, extracting objects from images and aligning them with entity and relation prompts for soft pseudo-labels. |
XUMING HU et. al. | arxiv-cs.CL | 2023-10-25 |
| 263 | Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a deep learning model tailored for document information analysis, emphasizing document classification, entity relation extraction, and document visual question answering. |
Tofik Ali; Partha Pratim Roy; | arxiv-cs.CV | 2023-10-25 |
| 264 | Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning. |
YICHAO CAO et. al. | nips | 2023-10-24 |
| 265 | RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. |
SHIAO MENG et. al. | arxiv-cs.CL | 2023-10-24 |
| 266 | CORD: A Three-Stage Coarse-to-Fine Framework for Relation Detection in Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and efficient three-stage framework to exploit the coarse-to-fine paradigm. |
Yanzeng Li; Sen Hu; Wenjuan Han; Lei Zou; | cikm | 2023-10-21 |
| 267 | Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It briefly discusses the main approaches and the pros and cons of each method. |
Serafina Kamp; Morteza Fayazi; Zineb Benameur-El; Shuyan Yu; Ronald Dreslinski; | arxiv-cs.IR | 2023-10-17 |
| 268 | Document-Level In-Context Few-Shot Relation Extraction Via Pre-Trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a remedy, we leverage the generalization capabilities of pre-trained LMs and present a novel framework for document-level in-context few-shot relation extraction. |
Yilmazcan Ozyurt; Stefan Feuerriegel; Ce Zhang; | arxiv-cs.CL | 2023-10-17 |
| 269 | Rethinking Relation Classification with Graph Meaning Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the growing interest, a critical gap persists in understanding the exact influence of GMRs, particularly concerning relation extraction tasks. Addressing this, we introduce DAGNN-plus, a simple and parameter-efficient neural architecture designed to decouple contextual representation learning from structural information propagation. |
Li Zhou; Wenyu Chen; Dingyi Zeng; Malu Zhang; Daniel Hershcovich; | arxiv-cs.CL | 2023-10-15 |
| 270 | PromptRE: Weakly-Supervised Document-Level Relation Extraction Via Prompting-Based Data Programming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Weakly-supervised document-level relation extraction faces significant challenges due to an imbalanced number no relation instances and the failure of directly probing pretrained large language models for document relation extraction. To address these challenges, we propose PromptRE, a novel weakly-supervised document-level relation extraction method that combines prompting-based techniques with data programming. |
Chufan Gao; Xulin Fan; Jimeng Sun; Xuan Wang; | arxiv-cs.CL | 2023-10-13 |
| 271 | Unsupervised Multimodal Learning for Image-text Relation Classification in Tweets Related Papers Related Patents Related Grants Related Venues Related Experts View |
Lin Sun; Qingyuan Li; Long Liu; Yindu Su; | Pattern Analysis and Applications | 2023-10-10 |
| 272 | Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Methods We developed a soft prompt-based LLM model and compared 4 training strategies including (1) fine-tuning without prompts; (2) hard-prompt with unfrozen LLMs; (3) soft-prompt with unfrozen LLMs; and (4) soft-prompt with frozen LLMs. |
CHENG PENG et. al. | arxiv-cs.CL | 2023-10-09 |
| 273 | Revisiting Large Language Models As Zero-shot Relation Extractors IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the summarize-and-ask (\textsc{SumAsk}) prompting, a simple prompt recursively using LLMs to transform RE inputs to the effective question answering (QA) format. |
Guozheng Li; Peng Wang; Wenjun Ke; | arxiv-cs.AI | 2023-10-08 |
| 274 | Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary Relational Knowledge Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. |
HAORAN LUO et. al. | arxiv-cs.AI | 2023-10-08 |
| 275 | Distantly-Supervised Joint Extraction with Noise-Robust Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose DENRL, a generalizable framework that 1) incorporates a lightweight transformer backbone into a sequence labeling scheme for joint tagging, and 2) employs a noise-robust framework that regularizes the tagging model with significant relation patterns and entity-relation dependencies, then iteratively self-adapts to instances with less noise from both sources. |
YUFEI LI et. al. | arxiv-cs.CL | 2023-10-07 |
| 276 | Extraction of Medication and Temporal Relation from Clinical Text Using Neural Language Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Clinical texts, represented in electronic medical records (EMRs), contain rich medical information and are essential for disease prediction, personalised information … |
Hangyu Tu; Lifeng Han; Goran Nenadic; | arxiv-cs.CL | 2023-10-03 |
| 277 | Do The Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we benchmark state-of-the-art pipeline and joint extraction models on sentence-level as well as document-level datasets. |
Pratik Saini; Tapas Nayak; Indrajit Bhattacharya; | arxiv-cs.CL | 2023-10-01 |
| 278 | A Lightweight Approach Based on Prompt for Few-shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ying Zhang; Wencheng Huang; Depeng Dang; | Comput. Speech Lang. | 2023-10-01 |
| 279 | Document-level Relation Extraction with Entity Mentions Deep Attention Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yangsheng Xu; Jiaxin Tian; Mingwei Tang; Linping Tao; Liuxuan Wang; | Comput. Speech Lang. | 2023-10-01 |
| 280 | Hyperplane Projection Network for Few-shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
WEI WANG et. al. | Expert Syst. Appl. | 2023-10-01 |
| 281 | Active Learning for Cross-sentence N-ary Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
SEUNGMIN SEO et. al. | Inf. Sci. | 2023-10-01 |
| 282 | Tuning N-ary Relation Extraction As Machine Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View |
PENGRUI REN et. al. | Neurocomputing | 2023-10-01 |
| 283 | CDTier: A Chinese Dataset of Threat Intelligence Entity Relationships Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Cyber Threat Intelligence (CTI), which is knowledge of cyberspace threats gathered from security data, is critical in defending against cyberattacks.However, there is no … |
YINGHAI ZHOU et. al. | IEEE Transactions on Sustainable Computing | 2023-10-01 |
| 284 | Siamese Representation Learning for Unsupervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, fine-grained relational semantic in relationship makes spurious negative samples, damaging the inherent hierarchical structure and hindering performances. To tackle this problem, we propose Siamese Representation Learning for Unsupervised Relation Extraction — a novel framework to simply leverage positive pairs to representation learning, possessing the capability to effectively optimize relation representation of instances and retain hierarchical information in relational feature space. |
Guangxin Zhang; Shu Chen; | arxiv-cs.CL | 2023-09-30 |
| 285 | A Comprehensive Survey of Document-level Relation Extraction (2016-2023) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This task has gained increased interest as a viable solution to build and populate knowledge bases automatically from unstructured large-scale documents (e.g., scientific papers, legal contracts, or news articles), in order to have a better understanding of relationships between entities. This paper aims to provide a comprehensive overview of recent advances in this field, highlighting its different applications in comparison to sentence-level relation extraction. |
JULIEN DELAUNAY et. al. | arxiv-cs.CL | 2023-09-28 |
| 286 | Relation Extraction: Hypernymy Discovery Using A Novel Pattern Learning Algorithm Related Papers Related Patents Related Grants Related Venues Related Experts View |
Olan Pinto; Shraddha Gole; H. P. Srushti; Anand Kumar Madasamy; | SN Computer Science | 2023-09-26 |
| 287 | OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To facilitate related research and application, we present an event understanding toolkit OmniEvent, which features three desiderata: (1) Comprehensive. |
HAO PENG et. al. | arxiv-cs.CL | 2023-09-25 |
| 288 | PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we approach the problem from a calibration perspective and propose PRiSM, which learns to adapt logits based on relation semantic information. |
Minseok Choi; Hyesu Lim; Jaegul Choo; | arxiv-cs.CL | 2023-09-25 |
| 289 | Multiple Relations Classification Using Imbalanced Predictions Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The pattern arises from the presence of a few valid relations that need positive labeling in a relatively large predefined relations set. We propose a multiple relations classification model that tackles these issues through a customized output architecture and by exploiting additional input features. |
Sakher Khalil Alqaaidi; Elika Bozorgi; Krzysztof J. Kochut; | arxiv-cs.CL | 2023-09-24 |
| 290 | GPBP: Pipeline Extraction of Entities and Relations for Construction of Urban Utility Tunnel Knowledge Graph Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In order to ensure the stable operation of the urban utility tunnel, the knowledge graphs can be applied to its inspection and maintenance process. Knowledge extraction is a key … |
JIYU CHEN et. al. | 2023 CAA Symposium on Fault Detection, Supervision and … | 2023-09-22 |
| 291 | ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. |
ZHILEI HU et. al. | arxiv-cs.CL | 2023-09-22 |
| 292 | BitCoin: Bidirectional Tagging and Supervised Contrastive Learning Based Joint Relational Triple Extraction Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They solely focus on extracting relational triples from subject to object, neglecting that once the extraction of a subject fails, it fails in extracting all triples associated with that subject. To address these issues, we propose BitCoin, an innovative Bidirectional tagging and supervised Contrastive learning based joint relational triple extraction framework. |
Luyao He; Zhongbao Zhang; Sen Su; Yuxin Chen; | arxiv-cs.CL | 2023-09-21 |
| 293 | Distantly Supervised Relation Extraction Via Contextual Information Interaction and Relation Embeddings Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Distantly supervised relation extraction (DSRE) utilizes an external knowledge base to automatically label a corpus, which inevitably leads to the problem of mislabeling. Existing … |
Huixin Yin; Shengquan Liu; Zhaorui Jian; | Symmetry | 2023-09-18 |
| 294 | Dealing with Negative Samples with Multi-task Learning on Span-based Joint Entity-relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models encounter a significant number of non-entity spans or irrelevant span pairs during the tasks, impairing model performance significantly. To address this issue, this paper introduces a span-based multitask entity-relation joint extraction model. |
Chenguang Xue; Jiamin Lu; | arxiv-cs.CL | 2023-09-18 |
| 295 | Comparative Analysis of Contextual Relation Extraction Based on Deep Learning Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, deep learning techniques have been used to identify the appropriate semantic relation based on the context from multiple sentences. |
R. Priyadharshini; G. Jeyakodi; P. Shanthi Bala; | arxiv-cs.CL | 2023-09-13 |
| 296 | Weighted-Dependency with Attention-Based Graph Convolutional Network for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yihao Dong; Xiaolong Xu; | Neural Processing Letters | 2023-09-09 |
| 297 | ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation Extraction (RE) is the task of extracting semantic relationships between entities in a sentence and aligning them to relations defined in a vocabulary, which is generally … |
Monika Jain; Kuldeep Singh; Raghava Mutharaju; | ECML/PKDD | 2023-09-04 |
| 298 | Exploring Pair-Aware Triangular Attention for Biomedical Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Biomedical relation extraction (BioRE) has become a research hotspot recently due to its crucial role in facilitating clinical diagnosis, treatment, and medical discovery. The … |
Lei Chen; J. Su; T. Lam; Ruibang Luo; | Proceedings of the 14th ACM International Conference on … | 2023-09-03 |
| 299 | Noun Compound Interpretation With Relation Classification and Paraphrasing Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Noun compounds are abundant in various languages and their interpretations have been applied in a wide range of NLP tasks. However, most existing work only uses relation … |
JINGPING LIU et. al. | IEEE Transactions on Knowledge and Data Engineering | 2023-09-01 |
| 300 | Syntax-based Dynamic Latent Graph for Event Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhu Ling; Hao Fei; Po Hu; | Inf. Process. Manag. | 2023-09-01 |
| 301 | Towards Hard Few-Shot Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Few-shot relation classification (FSRC) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for … |
Jiale Han; Bo Cheng; Zhiguo Wan; Wei Lu; | IEEE Transactions on Knowledge and Data Engineering | 2023-09-01 |
| 302 | Collective Prompt Tuning with Relation Inference for Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Changsen Yuan; Yixin Cao; Heyan Huang; | Inf. Process. Manag. | 2023-09-01 |
| 303 | GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we leverage the intrinsic graph processing capabilities of the Transformer model and propose replacing hand-coded pooling methods with new tokens in the input, which are designed to aggregate information via explicit graph relations in the computation of attention weights. |
Andrei C. Coman; Christos Theodoropoulos; Marie-Francine Moens; James Henderson; | arxiv-cs.CL | 2023-08-28 |
| 304 | SCoRD: Subject-Conditional Relation Detection with Text-Augmented Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the Open Images dataset, we propose a challenging OIv6-SCoRD benchmark such that the training and testing splits have a distribution shift in terms of the occurrence statistics of $\langle$subject, relation, object$\rangle$ triplets. To solve this problem, we propose an auto-regressive model that given a subject, it predicts its relations, objects, and object locations by casting this output as a sequence of tokens. |
ZIYAN YANG et. al. | arxiv-cs.CV | 2023-08-24 |
| 305 | Exploring Effective Inter-Encoder Semantic Interaction for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a document-level RE model with a Graph-Transformer Network (GTN). |
LIANG ZHANG et. al. | ijcai | 2023-08-23 |
| 306 | Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network for Spatial-Temporal Action Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network (AMCRNet) that extracts multi-scale context through multiple pooling layers with different sizes. |
JUN YU et. al. | ijcai | 2023-08-23 |
| 307 | RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including two novel tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). |
YANG BAI et. al. | ijcai | 2023-08-23 |
| 308 | CARE: Co-Attention Network for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing joint extraction methods suffer from issues of feature confusion or inadequate interaction between the two subtasks. Addressing these challenges, in this work, we propose a Co-Attention network for joint entity and Relation Extraction (CARE). |
Wenjun Kong; Yamei Xia; | arxiv-cs.CL | 2023-08-23 |
| 309 | PasCore: A Chinese Overlapping Relation Extraction Model Based on Global Pointer Annotation Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Especially, overlapping entities are common and indispensable in Chinese. To address this issue, this paper proposes PasCore, which utilizes a global pointer annotation strategy for overlapping relation extraction in Chinese. |
Peng Wang; Jiafeng Xie; Xiye Chen; Guozheng Li; Wei Li; | ijcai | 2023-08-23 |
| 310 | Is Prompt The Future?: A Survey of Evolution of Relation Extraction Approach Using Deep Learning and Big Data Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A vast amount of unstructured data is being generated in the age of big data. Relation extraction (RE) is the critical way to improve the utility of the data by extracting … |
ZHEN ZHU et. al. | Int. J. Inf. Technol. Syst. Approach | 2023-08-18 |
| 311 | Action Class Relation Detection and Classification Across Multiple Video Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a unified model to predict relations between action classes, using language and visual information associated with classes. |
Yuya Yoshikawa; Yutaro Shigeto; Masashi Shimbo; Akikazu Takeuchi; | arxiv-cs.CV | 2023-08-14 |
| 312 | Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph Related Papers Related Patents Related Grants Related Venues Related Experts View |
Quynh-Trang Pham Thi; Quang Huy Dao; Anh Duc Nguyen; T. Dang; | International Journal of Computational Intelligence Systems | 2023-08-11 |
| 313 | Prototypical Networks with Dual Attention and Regularization for Few-Shot Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction is a vital subtask of information extraction. It is also an important component for constructing the knowledge graphs. The purpose of relation extraction is to … |
Bei Liu; Wan Tao; Sanming Liu; | 2023 10th International Conference on Dependable Systems … | 2023-08-10 |
| 314 | Sequence Tagging with A Rethinking Structure for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
DAOJIAN ZENG et. al. | Int. J. Mach. Learn. Cybern. | 2023-08-10 |
| 315 | RadGraph2: Modeling Disease Progression in Radiology Reports Via Hierarchical Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present RadGraph2, a novel dataset for extracting information from radiology reports that focuses on capturing changes in disease state and device placement over time. |
SAMEER KHANNA et. al. | arxiv-cs.CL | 2023-08-09 |
| 316 | Ahead of The Text: Leveraging Entity Preposition for Financial Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result of our methodology, we achieved the 1st place ranking on the competition’s public leaderboard. |
Stefan Pasch; Dimitrios Petridis; | arxiv-cs.CL | 2023-08-08 |
| 317 | DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces a new benchmark dataset DialogRE^C+, introducing coreference resolution into the DRE scenario. |
YIYUN XIONG et. al. | arxiv-cs.CL | 2023-08-08 |
| 318 | Explaining Relation Classification Models with Semantic Extents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. |
Lars Klöser; Andre Büsgen; Philipp Kohl; Bodo Kraft; Albert Zündorf; | arxiv-cs.CL | 2023-08-04 |
| 319 | Multi-information Interaction Graph Neural Network for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
YINI ZHANG et. al. | Expert Syst. Appl. | 2023-08-01 |
| 320 | Document-level Relation Extraction with Hierarchical Dependency Tree and Bridge Path Related Papers Related Patents Related Grants Related Venues Related Experts View |
QIAN WAN et. al. | Knowl. Based Syst. | 2023-08-01 |
| 321 | Biomedical Relation Extraction with Knowledge Base–refined Weak Supervision Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Biomedical relation extraction (BioRE) is the task of automatically extracting and classifying relations between two biomedical entities in biomedical literature. Recent advances … |
WONJIN YOON et. al. | Database: The Journal of Biological Databases and Curation | 2023-07-26 |
| 322 | FinTree: Financial Dataset Pretrain Transformer Encoder for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present FinTree, Financial Dataset Pretrain Transformer Encoder for Relation Extraction. |
Hyunjong Ok; | arxiv-cs.CL | 2023-07-25 |
| 323 | Think Rationally About What You See: Continuous Rationale Extraction for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. |
Xuming Hu; Zhaochen Hong; Chenwei Zhang; Irwin King; Philip Yu; | sigir | 2023-07-25 |
| 324 | Reducing Spurious Correlations for Relation Extraction By Feature Decomposition and Semantic Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a spurious correlation reduction method for RE via feature decomposition and semantic augmentation (denoted as FDSA). |
Tianshu Yu; Min Yang; Chengming Li; Ruifeng Xu; | sigir | 2023-07-25 |
| 325 | SelfLRE: Self-refining Representation Learning for Low-resource Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we propose a novel LRE architecture named SelfLRE, which leverages two complementary modules, one module uses self-training to obtain pseudo-labels for unlabeled data, and the other module uses self-ensembling learning to obtain the task-agnostic representations, and leverages the existing pseudo-labels to refine the better task-specific representations on unlabeled data. |
Xuming Hu; Junzhe Chen; Shiao Meng; Lijie Wen; Philip S. Yu; | sigir | 2023-07-25 |
| 326 | REFinD: Relation Extraction Financial Dataset IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with ~29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. |
SIMERJOT KAUR et. al. | sigir | 2023-07-25 |
| 327 | Personalized Federated Relation Classification Over Heterogeneous Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to investigate personalized federated relation classification, in which strong client models adapted to their own data are desired. |
Ning Pang; Xiang Zhao; Weixin Zeng; Ji Wang; Weidong Xiao; | sigir | 2023-07-25 |
| 328 | Exploiting Ubiquitous Mentions for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address it, we propose to incorporate coreferences (e.g. pronouns and common nouns) into mentions, based on which we refine and re-annotate the widely-used DocRED benchmark as R-DocRED. |
Ruoyu Zhang; Yanzeng Li; Minhao Zhang; Lei Zou; | sigir | 2023-07-25 |
| 329 | Multimodal Named Entity Recognition and Relation Extraction with Retrieval-Augmented Strategy IF:7 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE) are tasks in information retrieval that aim to recognize entities and extract relations among … |
Xuming Hu; | Proceedings of the 46th International ACM SIGIR Conference … | 2023-07-18 |
| 330 | In Defense of Clip-based Video Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Hierarchical Context Model (HCM) that enriches the object-based spatial context and relation-based temporal context based on clips. |
Meng Wei; Long Chen; Wei Ji; Xiaoyu Yue; Roger Zimmermann; | arxiv-cs.CV | 2023-07-18 |
| 331 | Document-level Relation Extraction Via Separate Relation Representation and Logical Reasoning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new … |
Heyan Huang; Changsen Yuan; Qian Liu; Yixin Cao; | ACM Transactions on Information Systems | 2023-07-14 |
| 332 | Relational Extraction on Wikipedia Tables Using Convolutional and Memory Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a new model consisting of Convolutional Neural Network (CNN) and Bidirectional-Long Short Term Memory (BiLSTM) network to encode entities and learn dependencies among them, respectively. |
Arif Shahriar; Rohan Saha; Denilson Barbosa; | arxiv-cs.CL | 2023-07-11 |
| 333 | Entity Identifier: A Natural Text Parsing-based Framework For Entity Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we use natural language processing techniques to extract structured information from requirements descriptions, in order to automate the generation of CRUD (Create, Read, Update, Delete) class code. To facilitate this process, we introduce a pipeline for extracting entity and relation information, as well as a representation called an Entity Tree to model this information. |
El Mehdi Chouham; Jessica López Espejel; Mahaman Sanoussi Yahaya Alassan; Walid Dahhane; El Hassane Ettifouri; | arxiv-cs.CL | 2023-07-10 |
| 334 | HistRED: A Historical Document-Level Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To demonstrate the usefulness of our dataset, we propose a bilingual RE model that leverages both Korean and Hanja contexts to predict relations between entities. |
Soyoung Yang; Minseok Choi; Youngwoo Cho; Jaegul Choo; | arxiv-cs.CL | 2023-07-09 |
| 335 | More Than Classification: A Unified Framework for Event Temporal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs. |
QUZHE HUANG et. al. | acl | 2023-07-08 |
| 336 | S2ynRE: Two-stage Self-training with Synthetic Data for Low-resource Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose S2ynRE, a framework of two-stage Self-training with Synthetic data for Relation Extraction. |
BENFENG XU et. al. | acl | 2023-07-08 |
| 337 | Continual Contrastive Finetuning Improves Low-Resource Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim at bridging the gap and propose to pretrain and finetune the RE model using consistent objectives of contrastive learning. |
Wenxuan Zhou; Sheng Zhang; Tristan Naumann; Muhao Chen; Hoifung Poon; | acl | 2023-07-08 |
| 338 | UTC-IE: A Unified Token-pair Classification Architecture for Information Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the reformulation, we propose a Unified Token-pair Classification architecture for Information Extraction (UTC-IE), where we introduce Plusformer on top of the token-pair feature matrix. |
HANG YAN et. al. | acl | 2023-07-08 |
| 339 | No Clues Good Clues: Out of Context Lexical Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are indications that commonly used PTLMs already encode enough linguistic knowledge to allow the use of minimal (or none) textual context for some linguistically motivated tasks, thus notably reducing human effort, the need for data pre-processing, and favoring techniques that are language neutral since do not rely on syntactic structures. In this work, we explore this idea for the tasks of lexical relation classification (LRC) and graded Lexical Entailment (LE). |
Lucia Pitarch; Jordi Bernad; Lacramioara Dranca; Carlos Bobed Lisbona; Jorge Gracia; | acl | 2023-07-08 |
| 340 | Did The Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Document-level relation extraction (DocRE) attracts more research interest recently. While models achieve consistent performance gains in DocRE, their underlying decision rules are still understudied: Do they make the right predictions according to rationales? In this paper, we take the first step toward answering this question and then introduce a new perspective on comprehensively evaluating a model. |
Haotian Chen; Bingsheng Chen; Xiangdong Zhou; | acl | 2023-07-08 |
| 341 | Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Implicit discourse relation classification is a challenging task due to the absence of discourse connectives. To overcome this issue, we design an end-to-end neural model to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB. |
Wei Liu; Michael Strube; | acl | 2023-07-08 |
| 342 | Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate the issues, we propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction, which captures the global structure information between individual tasks and exploits interactions within unlabeled data. |
Yandan Zheng; Anran Hao; Anh Tuan Luu; | acl | 2023-07-08 |
| 343 | Joint Constrained Learning with Boundary-adjusting for Emotion-Cause Pair Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a **J**oint **C**onstrained Learning framework with **B**oundary-adjusting for Emotion-Cause Pair Extraction (**JCB**). |
HUAWEN FENG et. al. | acl | 2023-07-08 |
| 344 | Rethinking Multimodal Entity and Relation Extraction from A Translation Point of View IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We revisit the multimodal entity and relation extraction from a translation point of view. |
Changmeng Zheng; Junhao Feng; Yi Cai; Xiaoyong Wei; Qing Li; | acl | 2023-07-08 |
| 345 | MultiTACRED: A Multilingual Version of The TAC Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al. , 2017). To address this gap, we introduce the MultiTACRED dataset, covering 12 typologically diverse languages from 9 language families, which is created by machine-translating TACRED instances and automatically projecting their entity annotations. |
Leonhard Hennig; Philippe Thomas; Sebastian M�ller; | acl | 2023-07-08 |
| 346 | Linguistic Representations for Fewer-shot Relation Extraction Across Domains IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. |
Sireesh Gururaja; Ritam Dutt; Tinglong Liao; Carolyn Ros�; | acl | 2023-07-08 |
| 347 | Direct Fact Retrieval from Knowledge Graphs Without Entity Linking IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this approach requires additional labels for training each of the three subcomponents in addition to pairs of input texts and facts, and also may accumulate errors propagated from failures in previous steps. To tackle these limitations, we propose a simple knowledge retrieval framework, which directly retrieves facts from the KGs given the input text based on their representational similarities, which we refer to as Direct Fact Retrieval (DiFaR). |
Jinheon Baek; Alham Fikri Aji; Jens Lehmann; Sung Ju Hwang; | acl | 2023-07-08 |
| 348 | Revisiting Relation Extraction in The Era of Large Language Models IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. |
Somin Wadhwa; Silvio Amir; Byron Wallace; | acl | 2023-07-08 |
| 349 | Improving Continual Relation Extraction By Distinguishing Analogous Semantics IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct an empirical study on existing works and observe that their performance is severely affected by analogous relations. To address this issue, we propose a novel continual extraction model for analogous relations. |
Wenzheng Zhao; Yuanning Cui; Wei Hu; | acl | 2023-07-08 |
| 350 | UniEX: An Effective and Efficient Framework for Unified Information Extraction Via A Span-extractive Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. |
YANG PING et. al. | acl | 2023-07-08 |
| 351 | RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a fine-grained semantic matching method tailored for zero-shot relation extraction. |
JUN ZHAO et. al. | acl | 2023-07-08 |
| 352 | Consistent Prototype Learning for Few-Shot Continual Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a new N-way-K-shot Continual Relation Extraction (NK-CRE) task and propose a novel few-shot continual relation extraction method with Consistent Prototype Learning (ConPL) to address the aforementioned issues. |
Xiudi Chen; Hui Wu; Xiaodong Shi; | acl | 2023-07-08 |
| 353 | Actively Supervised Clustering for Open Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel setting, named actively supervised clustering for OpenRE. |
JUN ZHAO et. al. | acl | 2023-07-08 |
| 354 | REDFM: A Filtered and Multilingual Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English. In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems. |
?Pere-Llu�s Huguet Cabot; Simone Tedeschi; Axel-Cyrille Ngonga Ngomo; Roberto Navigli; | acl | 2023-07-08 |
| 355 | Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a Document-level distant Relation Extraction framework with Uncertainty Guided label denoising, UGDRE. |
QI SUN et. al. | acl | 2023-07-08 |
| 356 | Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve the prediction, this research proposes to retrieve textual and visual evidence based on the object, sentence, and whole image. |
Xuming Hu; Zhijiang Guo; Zhiyang Teng; Irwin King; Philip S. Yu; | acl | 2023-07-08 |
| 357 | Information Screening Whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and external-information exploiting. |
Shengqiong Wu; Hao Fei; Yixin Cao; Lidong Bing; Tat-Seng Chua; | acl | 2023-07-08 |
| 358 | A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications. To overcome this limitation, we propose TaG, a novel table-to-graph generation model for joint extractionof entities and relations at document-level. |
Ruoyu Zhang; Yanzeng Li; Lei Zou; | acl | 2023-07-08 |
| 359 | Open Set Relation Extraction Via Unknown-Aware Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an unknown-aware training method, regularizing the model by dynamically synthesizing negative instances that can provide the missing supervision signals. |
JUN ZHAO et. al. | acl | 2023-07-08 |
| 360 | A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Though discourse parsing can help multiple NLP fields, there has been no wide language model search done on implicit discourse relation classification. This hinders researchers from fully utilizing public-available models in discourse analysis. |
Bruce W. Lee; BongSeok Yang; Jason Hyung-Jong Lee; | arxiv-cs.CL | 2023-07-07 |
| 361 | Linguistic Representations for Fewer-shot Relation Extraction Across Domains IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. |
Sireesh Gururaja; Ritam Dutt; Tinglong Liao; Carolyn Rose; | arxiv-cs.CL | 2023-07-07 |
| 362 | Hybrid Enhancement-based Prototypical Networks for Few-shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
LEI WANG et. al. | World Wide Web | 2023-07-03 |
| 363 | Improving Long-tail Relation Extraction Via Adaptive Adjustment and Causal Inference Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jingyao Tang; Lishuang Li; Hongbin Lu; Beibei Zhang; Haiming Wu; | Neurocomputing | 2023-07-01 |
| 364 | Mining Heuristic Evidence Sentences for More Interpretable Document-level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
TAOJIE ZHU et. al. | J. King Saud Univ. Comput. Inf. Sci. | 2023-07-01 |
| 365 | Enhancing Interaction Representation for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ruixue Tang; Yanping Chen; Ruizhang Huang; Yongbin Qin; | Cognitive Systems Research | 2023-07-01 |
| 366 | Knowledge-enhanced Event Relation Extraction Via Event Ontology Prompt Related Papers Related Patents Related Grants Related Venues Related Experts View |
L. Zhuang; Hao Fei; Po Hu; | Inf. Fusion | 2023-07-01 |
| 367 | A Unified MRC Framework with Multi-Query for Multi-modal Relation Triplets Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation triplets extraction (RTE) aims to extract all potential triplets of subject-object entities and the corresponding relation in a sentence, which is an extremely essential … |
Qiang Chen; Dong Zhang; Shoushan Li; Guodong Zhou; | 2023 IEEE International Conference on Multimedia and Expo … | 2023-07-01 |
| 368 | Local-to-Global Causal Reasoning for Cross-Document Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Cross-document relation extraction (RE), as an extension of information extraction, requires integrating information from multiple documents retrieved from open domains with a … |
HAO WU et. al. | IEEE/CAA Journal of Automatica Sinica | 2023-07-01 |
| 369 | GPT-FinRE: In-context Learning for Financial Relation Extraction Using Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In the financial domain, relation extraction plays a vital role in extracting valuable information from financial documents, such as news articles, earnings reports, and company filings. This paper describes our solution to relation extraction on one such dataset REFinD. |
Pawan Kumar Rajpoot; Ankur Parikh; | arxiv-cs.CL | 2023-06-30 |
| 370 | IMETRE: Incorporating Markers of Entity Types for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we approach the task of relationship extraction in the financial dataset REFinD. |
N Harsha Vardhan; Manav Chaudhary; | arxiv-cs.CL | 2023-06-30 |
| 371 | Document-Level Relation Extraction with Local Relation and Global Inference Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The current popular approach to the extraction of document-level relations is mainly based on either a graph structure or serialization model method for the inference, but the … |
Yiming Liu; Hongtao Shan; Feng Nie; Gaoyu Zhang; G. Yuan; | Inf. | 2023-06-27 |
| 372 | Linking People Across Text and Images Based on Social Relation Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We observe that humans are adept at exploring social relations to assist identifying people. Therefore, we propose a Social Relation Reasoning (SRR) model to address the aforementioned issues. |
Yang Lei; Peizhi Zhao; Pijian Li; Yi Cai; Qingbao Huang; | aaai | 2023-06-26 |
| 373 | Improving Distantly Supervised Relation Extraction By Natural Language Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. |
Kang Zhou; Qiao Qiao; Yuepei Li; Qi Li; | aaai | 2023-06-26 |
| 374 | FmLRE: A Low-Resource Relation Extraction Model Based on Feature Mapping Similarity Calculation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing work takes advantages of self-training or distant supervision to expand the limited labeled data in the data-driven approaches, while the selection bias of pseudo labels may cause the error accumulation in subsequent relation classification. To address this issue, this paper proposes fmLRE, an iterative feedback method based on feature mapping similarity calculation to improve the accuracy of pseudo labels. |
Peng Wang; Tong Shao; Ke Ji; Guozheng Li; Wenjun Ke; | aaai | 2023-06-26 |
| 375 | Joint Multimodal Entity-Relation Extraction Based on Edge-Enhanced Graph Alignment Network and Word-Pair Relation Tagging IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for the JMERE task. |
Li Yuan; Yi Cai; Jin Wang; Qing Li; | aaai | 2023-06-26 |
| 376 | Sequence Generation with Label Augmentation for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. |
Bo Li; Dingyao Yu; Wei Ye; Jinglei Zhang; Shikun Zhang; | aaai | 2023-06-26 |
| 377 | Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. |
Bo Li; Wei Ye; Jinglei Zhang; Shikun Zhang; | aaai | 2023-06-26 |
| 378 | RankDNN: Learning to Rank for Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. |
QIANYU GUO et. al. | aaai | 2023-06-26 |
| 379 | Competition or Cooperation? Exploring Unlabeled Data Via Challenging Minimax Game for Semi-supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the consensus of two modules greatly restricts the model from exploring diverse relation expressions in unlabeled set, which hinders the performance as well as model generalization. To tackle this problem, in this paper, we propose a novel competition-based method AdvSRE. |
YU HONG et. al. | aaai | 2023-06-26 |
| 380 | KICE: A Knowledge Consolidation and Expansion Framework for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the collaboration among different knowledge sources and present KICE, a Knowledge-evolving framework by Iterative Consolidation and Expansion with the guidance of PLMs and rule-based patterns. |
Yilin Lu; Xiaoqiang Wang; Haofeng Yang; Siliang Tang; | aaai | 2023-06-26 |
| 381 | Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we explore distilling the reasoning ability of large language models (LLMs) into a more compact student model by generating a \textit{chain of thought} (CoT) — a sequence of intermediate reasoning steps. |
Feng Chen; Yujian Feng; | arxiv-cs.CL | 2023-06-25 |
| 382 | Mutually Guided Few-shot Learning for Relational Triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). |
Chengmei Yang; Shuai Jiang; Bowei He; Chen Ma; Lianghua He; | arxiv-cs.CL | 2023-06-23 |
| 383 | Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects, so as to mine more helpful information for the task, termed as DGF-PT. |
QIAN LI et. al. | arxiv-cs.CL | 2023-06-19 |
| 384 | Computable Contracts By Extracting Obligation Logic Graphs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The emergence of contract specific programming languages has struggled to translate into widespread adoption of computable contracts due largely to high conversion costs. In this … |
SERGIO SERVANTEZ et. al. | Proceedings of the Nineteenth International Conference on … | 2023-06-19 |
| 385 | BioREx: Improving Biomedical Relation Extraction By Leveraging Heterogeneous Datasets IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. |
Po-Ting Lai; Chih-Hsuan Wei; Ling Luo; Qingyu Chen; Zhiyong Lu; | arxiv-cs.CL | 2023-06-19 |
| 386 | Modeling Zero-Shot Relation Classification As A Multiple-Choice Problem Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Zero-shot relation classification (ZeroRC) aims to infer the semantic relations between entity pairs in sentences, while the relation sets at the training and testing stages are … |
Yuquan Lan; Dongxu Li; Yunqi Zhang; Hui Zhao; Gang Zhao; | 2023 International Joint Conference on Neural Networks … | 2023-06-18 |
| 387 | An Improved Relation Extraction Method Based on Information Control Injection and Attention-Guided Densely Connected Graph Convolutional Network Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The extraction of entities and multi-type relations in overlapping triples has been a challenging problem. This paper proposes a relation extraction method based on information … |
ZIRUI ZHANG et. al. | 2023 International Joint Conference on Neural Networks … | 2023-06-18 |
| 388 | Chinese Event Temporal Relation Extraction on Multi-Dimensional Attention Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting event temporal relations is an important task of natural language processing. It is even more challenging to extract the temporal relations of events without using … |
Fan Yang; Sheng Xu; Peifeng Li; Qiaoming Zhu; | 2023 International Joint Conference on Neural Networks … | 2023-06-18 |
| 389 | Knowledge Graph Enhanced Sentential Relation Extraction Via Dual Heterogeneous Graph Context Selection Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Sentential relation extraction is a type of relation extraction task whose goal is to extract semantic relations between entities from a single sentence. Compared with other … |
BO XU et. al. | 2023 International Joint Conference on Neural Networks … | 2023-06-18 |
| 390 | A Joint Entity and Relation Extraction Model Based on Efficient Sampling and Explicit Interaction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint entity and relation extraction (RE) construct a framework for unifying entity recognition and relationship extraction, and the approach can exploit the dependencies between … |
Qibin Li; Nianmin Yao; Nai Zhou; Jianming Zhao; Yanan Zhang; | ACM Transactions on Intelligent Systems and Technology | 2023-06-17 |
| 391 | REDFM: A Filtered and Multilingual Relation Extraction Dataset IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural … |
Pere-Llu’is Huguet Cabot; Simone Tedeschi; A. N. Ngomo; Roberto Navigli; | Annual Meeting of the Association for Computational … | 2023-06-16 |
| 392 | RED$^{\rm FM}$: A Filtered and Multilingual Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English. In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems. |
Pere-Lluís Huguet Cabot; Simone Tedeschi; Axel-Cyrille Ngonga Ngomo; Roberto Navigli; | arxiv-cs.CL | 2023-06-16 |
| 393 | Semi-supervised Relation Extraction Via Data Augmentation and Consistency-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we leverage the recent advances in controlled text generation to perform high quality data augmentation for the RE task. |
Komal K. Teru; | arxiv-cs.CL | 2023-06-16 |
| 394 | Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, traditional self-training is vulnerable to confirmation bias and exhibits poor performance in minority classes. To overcome this limitation, we proposed a novel class-adaptive re-sampling self-training framework. |
Qingyu Tan; Lu Xu; Lidong Bing; Hwee Tou Ng; | arxiv-cs.CL | 2023-06-16 |
| 395 | Rethinking Document-Level Relation Extraction: A Reality Check IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we do not aim at proposing a novel model for DocRE. |
Jing Li; Yequan Wang; Shuai Zhang; Min Zhang; | arxiv-cs.CL | 2023-06-15 |
| 396 | Useful Cyber Threat Intelligence Relation Retrieval Using Transfer Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The emergence of hacker groups extends the complexity and frequency of cyberattacks. To adapt to the rapidly evolving cyberattacks, acquiring valuable information from security … |
Chia-Mei Chen; Fang-Hsuan Hsu; Jenq-Neng Hwang; | Proceedings of the 2023 European Interdisciplinary … | 2023-06-14 |
| 397 | Building A Corpus for Biomedical Relation Extraction of Species Mentions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a manually annotated corpus, Species-Species Interaction, for extracting meaningful binary relations between species, in biomedical texts, at sentence level, with a focus on the gut microbiota. |
Oumaima El Khettari; Solen Quiniou; Samuel Chaffron; | arxiv-cs.CL | 2023-06-14 |
| 398 | Zero-Shot Dialogue Relation Extraction By Relating Explainable Triggers and Relation Names Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Developing dialogue relation extraction (DRE) systems often requires a large amount of labeled data, which can be costly and time-consuming to annotate. In order to improve … |
Zenan Xu; Yun-Nung Chen; | ArXiv | 2023-06-09 |
| 399 | Open Set Relation Extraction Via Unknown-Aware Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an unknown-aware training method, regularizing the model by dynamically synthesizing negative instances. |
JUN ZHAO et. al. | arxiv-cs.CL | 2023-06-08 |
| 400 | Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create a high-performing model. |
Fréjus A. A. Laleye; Loïc Rakotoson; Sylvain Massip; | arxiv-cs.CL | 2023-06-07 |
| 401 | FinRED: A Dataset for Relation Extraction in Financial Domain Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we release FinRED, a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. |
SOUMYA SHARMA et. al. | arxiv-cs.CL | 2023-06-06 |
| 402 | Few-shot Relation Classification Based on The BERT Model, Hybrid Attention and Fusion Networks Related Papers Related Patents Related Grants Related Venues Related Experts View |
YIBING LI et. al. | Applied Intelligence | 2023-06-03 |
| 403 | A Comprehensive Survey on Deep Learning for Relation Extraction: Recent Advances and New Frontiers Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) involves identifying the relations between entities from unstructured texts. RE serves as the foundation for many natural language processing (NLP) … |
XIAOYAN ZHAO et. al. | ArXiv | 2023-06-03 |
| 404 | A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This survey provides a comprehensive review of existing deep learning techniques for RE. |
XIAOYAN ZHAO et. al. | arxiv-cs.CL | 2023-06-03 |
| 405 | Denoising Graph Inference Network for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Hailin Wang; Ke Qin; Guiduo Duan; Guangchun Luo; | Big Data Min. Anal. | 2023-06-01 |
| 406 | Distant Supervision for Relation Extraction with Hierarchical Attention-based Networks Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jing Zhang; Meilin Cao; | Expert Syst. Appl. | 2023-06-01 |
| 407 | GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. |
XUMING HU et. al. | arxiv-cs.CL | 2023-05-26 |
| 408 | ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. |
Trung Hoang Le; Huiping Cao; Tran Cao Son; | arxiv-cs.CL | 2023-05-24 |
| 409 | RS-TTS: A Novel Joint Entity and Relation Extraction Model Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint extraction of entity and relation is a basic task in the field of natural language processing. Existing methods have achieved good result, but there are still some … |
Jialu Zhang; Xingguo Jiang; Yan Sun; Hong Luo; | 2023 26th International Conference on Computer Supported … | 2023-05-24 |
| 410 | RE$^2$: Region-Aware Relation Extraction from Visually Rich Documents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose REgion-Aware Relation Extraction (RE$^2$) that leverages region-level spatial structure among the entity blocks to improve their relation prediction. |
Pritika Ramu; Sijia Wang; Lalla Mouatadid; Joy Rimchala; Lifu Huang; | arxiv-cs.CL | 2023-05-23 |
| 411 | Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The absence of global structure information may make the model struggle to learn long-range relations and easily predict conflicted results. To alleviate such limitations, we propose a GlObal Structure knowledge-guided relation Extraction (GOSE) framework. |
XIANGNAN CHEN et. al. | arxiv-cs.CL | 2023-05-23 |
| 412 | Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new approach, ReXMiner, for zero-shot relation extraction in web mining. |
Zilong Wang; Jingbo Shang; | arxiv-cs.CL | 2023-05-23 |
| 413 | SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energy-based Event-Centric Hyperspheres (SPEECH). |
Shumin Deng; Shengyu Mao; Ningyu Zhang; Bryan Hooi; | arxiv-cs.CL | 2023-05-22 |
| 414 | How Fragile Is Relation Extraction Under Entity Replacements? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we operate the random and type-constrained entity replacements over the RE instances in TACRED and evaluate the state-of-the-art RE models under the entity replacements. |
YIWEI WANG et. al. | arxiv-cs.CL | 2023-05-22 |
| 415 | Zero-shot Visual Relation Detection Via Composite Visual Cues from Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel method for zero-shot VRD: RECODE, which solves RElation detection via COmposite DEscription prompts. |
LIN LI et. al. | arxiv-cs.CV | 2023-05-21 |
| 416 | Causality Extraction Cascade Model Based on Dual Labeling Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Causal relation extraction is a crucial task in natural language processing. Current extraction methods have problems, including low accuracy of causal-event division and … |
Fengxiao Yan; Bo Shen; Chenyang Dai; | J. Adv. Comput. Intell. Intell. Informatics | 2023-05-20 |
| 417 | Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate an unbiased UD-based XRE transfer by constructing a type of code-mixed UD forest. |
Hao Fei; Meishan Zhang; Min Zhang; Tat-Seng Chua; | arxiv-cs.CL | 2023-05-20 |
| 418 | Silver Syntax Pre-training for Cross-Domain Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate intermediate pre-training specifically for RE. |
Elisa Bassignana; Filip Ginter; Sampo Pyysalo; Rob van der Goot; Barbara Plank; | arxiv-cs.CL | 2023-05-18 |
| 419 | Aligning Instruction Tasks Unlocks Large Language Models As Zero-Shot Relation Extractors IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We hypothesize that instruction-tuning has been unable to elicit strong RE capabilities in LLMs due to RE’s low incidence in instruction-tuning datasets, making up less than 1% of all tasks (Wang et al., 2022). To address this limitation, we propose QA4RE, a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets. |
Kai Zhang; Bernal Jiménez Gutiérrez; Yu Su; | arxiv-cs.CL | 2023-05-18 |
| 420 | Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Multi-CrossRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. |
Elisa Bassignana; Filip Ginter; Sampo Pyysalo; Rob van der Goot; Barbara Plank; | arxiv-cs.CL | 2023-05-18 |
| 421 | UniEX: An Effective and Efficient Framework for Unified Information Extraction Via A Span-extractive Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. |
PING YANG et. al. | arxiv-cs.CL | 2023-05-17 |
| 422 | An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. |
Zhao Xie; Tian Gao; Kewei Wu; Jiao Chang; | cvpr | 2023-05-17 |
| 423 | GeoLayoutLM: Geometric Pre-Training for Visual Information Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, we reveal another factor that limits the performance of RE lies in the objective gap between the pre-training phase and the fine-tuning phase for RE. To tackle these issues, we propose in this paper a multi-modal framework, named GeoLayoutLM, for VIE. |
Chuwei Luo; Changxu Cheng; Qi Zheng; Cong Yao; | cvpr | 2023-05-17 |
| 424 | About Evaluation of F1 Score for RECENT Relation Extraction System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This document contains a discussion of the F1 score evaluation used in the article ‘Relation Classification with Entity Type Restriction’ by Shengfei Lyu, Huanhuan Chen published on Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. |
Michał Olek; | arxiv-cs.CL | 2023-05-16 |
| 425 | Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Considering the ubiquity of noisy labels in real-world datasets, in this paper, we formalize a more practical learning scenario, termed as \textit{noisy-CRE}. |
TING WU et. al. | arxiv-cs.CL | 2023-05-11 |
| 426 | Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new model, namely SCKD, to accomplish the continual few-shot RE task. |
Xinyi Wang; Zitao Wang; Wei Hu; | arxiv-cs.CL | 2023-05-11 |
| 427 | Revisiting Relation Extraction in The Era of Large Language Models IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. |
Somin Wadhwa; Silvio Amir; Byron C. Wallace; | arxiv-cs.CL | 2023-05-08 |
| 428 | Enhancing Continual Relation Extraction Via Classifier Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. |
HEMING XIA et. al. | arxiv-cs.CL | 2023-05-08 |
| 429 | MultiTACRED: A Multilingual Version of The TAC Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACRED dataset, covering 12 typologically diverse languages from 9 language families, which is created by machine-translating TACRED instances and automatically projecting their entity annotations. |
Leonhard Hennig; Philippe Thomas; Sebastian Möller; | arxiv-cs.CL | 2023-05-08 |
| 430 | HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose HIORE, a new method for unified entity relation extraction. |
YIJUN WANG et. al. | arxiv-cs.CL | 2023-05-07 |
| 431 | Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Insights discussed in this study can help DL stakeholders select techniques for building optimal knowledge-graph-based systems. |
Ming Jiang; Jennifer D’Souza; Sören Auer; J. Stephen Downie; | arxiv-cs.DL | 2023-05-03 |
| 432 | How to Unleash The Power of Large Language Models for Few-shot Relation Extraction? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5 through exhaustive experiments. |
Xin Xu; Yuqi Zhu; Xiaohan Wang; Ningyu Zhang; | arxiv-cs.CL | 2023-05-02 |
| 433 | Think Rationally About What You See: Continuous Rationale Extraction for Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. … |
Xuming Hu; Zhaochen Hong; Chenwei Zhang; Irwin King; Philip S. Yu; | ArXiv | 2023-05-02 |
| 434 | A Novel Pipelined End-to-end Relation Extraction Framework with Entity Mentions and Contextual Semantic Representation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
ZHAORAN LIU et. al. | Expert Syst. Appl. | 2023-05-01 |
| 435 | Boundary Regression Model for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Ruixue Tang; Yanping Chen; Yongbin Qin; Ruizhang Huang; Qinghua Zheng; | Expert Syst. Appl. | 2023-05-01 |
| 436 | Granularity-Aware Area Prototypical Network With Bimargin Loss for Few Shot Relation Classification IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Classification is one of the most important tasks in text mining. Previous methods either require large-scale manually-annotated data or rely on distant supervision … |
Haopeng Ren; Yi Cai; Raymond Y. K. Lau; Ho-fung Leung; Qing Li; | IEEE Transactions on Knowledge and Data Engineering | 2023-05-01 |
| 437 | Constructing A Knowledge Graph from Textual Descriptions of Software Vulnerabilities in The National Vulnerability Database Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a new method for constructing a vulnerability knowledge graph from information in the National Vulnerability Database (NVD). |
Anders Mølmen Høst; Pierre Lison; Leon Moonen; | arxiv-cs.CR | 2023-04-30 |
| 438 | Towards Model Robustness: Generating Contextual Counterfactuals for Entities in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the problem of automatically generating CAD for RE tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. |
Mi Zhang; Tieyun Qian; Ting Zhang; Xin Miao; | www | 2023-04-29 |
| 439 | Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Compared with standard text, understanding dialogue is more challenging for machines as the dynamic and unexpected semantic changes in each turn. To model such inconsistent semantics, we propose a simple but effective Hierarchical Dialogue Understanding model, HiDialog. |
Xiao Liu; Jian Zhang; Heng Zhang; Fuzhao Xue; Yang You; | arxiv-cs.CL | 2023-04-29 |
| 440 | ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations. |
CHUNKIT CHAN et. al. | arxiv-cs.CL | 2023-04-28 |
| 441 | Asymmetric Polynomial Loss for Multi-Label Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the imbalance between redundant negative samples and rare positive samples could degrade the model performance. In this paper, we propose an effective Asymmetric Polynomial Loss (APL) to mitigate the above issues. |
Y. Huang; J. Qi; X. Wang; Z. Lin; | icassp | 2023-04-27 |
| 442 | Spammer Detection on Short Video Applications: A New Challenge and Baselines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new challenge of spammer detection on short video applications, where the multi-modal information of videos and reviews plays a more critical role than the spam relation graph. |
M. Yi; D. Liang; R. Wang; Y. Ding; H. Lu; | icassp | 2023-04-27 |
| 443 | Mutually Guided Few-Shot Learning For Relational Triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The performance will drop dramatically when only few labeled data are available. To tackle this problem, we propose the Mutually Guided Few-shot learning framework for Relational Triple Extraction (MG-FTE). |
C. Yang; S. Jiang; B. He; C. Ma; L. He; | icassp | 2023-04-27 |
| 444 | TABLEIE: Capturing The Interactions Among Sub-Tasks in Information Extraction Via Double Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a double-table framework, TableIE, to capture the interactions among IE sub-tasks as well as improve the model efficiency. |
J. Lin; R. Xu; B. Chang; | icassp | 2023-04-27 |
| 445 | CORSD: Class-Oriented Relational Self Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel training framework named Class-Oriented Relational Self Distillation (CORSD) to address the limitations. |
M. YU et. al. | icassp | 2023-04-27 |
| 446 | Commdre: Document-Level Relation Extraction with Self-Supervised Commonsense Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a self-supervised commonsense-enhanced DocRE model, called CommDRE, without external knowledge. |
R. LI et. al. | icassp | 2023-04-27 |
| 447 | DocRED-FE: A Document-Level Fine-Grained Entity and Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we construct a large-scale document-level fine-grained JERE dataset DocRED-FE, which improves DocRED with Fine-Grained Entity Type. |
H. WANG et. al. | icassp | 2023-04-27 |
| 448 | Relational Representation Learning for Zero-Shot Relation Extraction with Instance Prompting and Prototype Rectification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method based on Instance Prompting and Prototype Rectification (IPPR) to conduct relational representation learning for zeroshot relation extraction. |
B. Duan; X. Liu; S. Wang; Y. Xu; B. Xiao; | icassp | 2023-04-27 |
| 449 | BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conclusions : In this paper, we propose a novel end-to-end system for the construction of a biomedical knowledge graph from clinical textual using a variation of BERT models. |
AYOUB HARNOUNE et. al. | arxiv-cs.CL | 2023-04-21 |
| 450 | Prompt-Learning for Cross-Lingual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel XRE algorithm based on Prompt-Tuning, referred to as Prompt-XRE. |
CHIAMING HSU et. al. | arxiv-cs.CL | 2023-04-20 |
| 451 | Multi-task Learning for Few-shot Biomedical Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
V. Moscato; Giuseppe Napolano; Marco Postiglione; Giancarlo Sperlí; | Artificial Intelligence Review | 2023-04-19 |
| 452 | Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains. |
CHENG DENG et. al. | arxiv-cs.IR | 2023-04-14 |
| 453 | Semantic Relation Extraction: A Review of Approaches, Datasets, and Evaluation Methods With Looking at The Methods and Datasets in The Persian Language IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: A large volume of unstructured data, especially text data, is generated and exchanged daily. Consequently, the importance of extracting patterns and discovering knowledge from … |
Hamid Gharagozlou; J. Mohammadzadeh; A. Bastanfard; S. S. Ghidary; | ACM Transactions on Asian and Low-Resource Language … | 2023-04-14 |
| 454 | Zero-shot Temporal Relation Extraction with ChatGPT IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate ChatGPT’s ability on zero-shot temporal relation extraction. |
Chenhan Yuan; Qianqian Xie; Sophia Ananiadou; | arxiv-cs.CL | 2023-04-11 |
| 455 | Sentence-Level Relation Extraction Via Contrastive Learning with Descriptive Relation Prompts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new paradigm, Contrastive Learning with Descriptive Relation Prompts(CTL-DRP), to jointly consider entity information, relational knowledge and entity type restrictions. |
Jiewen Zheng; Ze Chen; | arxiv-cs.CL | 2023-04-10 |
| 456 | Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we conduct a comprehensive evaluation of state-of-the-art LLMs, namely GPT-3.5, GPT-4, and Bard, within the realm of clinical language understanding tasks. |
Yuqing Wang; Yun Zhao; Linda Petzold; | arxiv-cs.CL | 2023-04-09 |
| 457 | Learning Implicit and Explicit Multi-task Interactions for Information Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Information extraction aims at extracting entities, relations, and so on, in text to support information retrieval systems. To extract information, researchers have considered … |
Kai Sun; Richong Zhang; Samuel Mensah; Yongyi Mao; Xudong Liu; | ACM Transactions on Information Systems | 2023-04-08 |
| 458 | Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE by Multi-Modal representation learning with Information Bottleneck (MMIB). |
SHIYAO CUI et. al. | arxiv-cs.MM | 2023-04-05 |
| 459 | EDeR: A Dataset for Exploring Dependency Relations Between Events Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We argue that an important type of relation not explored in NLP or IR research to date is that of an event being an argument – required or optional – of another event. We introduce the human-annotated Event Dependency Relation dataset (EDeR) which provides this dependency relation. |
Ruiqi Li; Patrik Haslum; Leyang Cui; | arxiv-cs.CL | 2023-04-04 |
| 460 | End-to-End Models for Chemical–Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially. E2ERE … |
Xu-Xia Ai; Ramakanth Kavuluru; | 2023 IEEE 11th International Conference on Healthcare … | 2023-04-03 |
| 461 | End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this effort, we employ a span-based pipeline approach to produce a new state-of-the-art E2ERE performance on the ChemProt dataset, resulting in $> 4\%$ improvement in F1-score over the prior best effort. |
Xuguang Ai; Ramakanth Kavuluru; | arxiv-cs.CL | 2023-04-03 |
| 462 | Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we aim to achieve better integration of both the discriminability and robustness for the DocRE problem. |
Jia Guo; Stanley Kok; Lidong Bing; | arxiv-cs.CL | 2023-04-03 |
| 463 | End-to-End N-ary Relation Extraction for Combination Drug Therapies Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Combination drug therapies are treatment regimens that involve two or more drugs, administered more commonly for patients with cancer, HIV, malaria, or tuberculosis. Currently … |
Yuhang Jiang; Ramakanth Kavuluru; | 2023 IEEE 11th International Conference on Healthcare … | 2023-03-30 |
| 464 | End-to-End $n$-ary Relation Extraction for Combination Drug Therapies Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This is an absolute $\approx 5\%$ F1-score improvement even over the prior best relation classification score with spotted drug entities (hence, not end-to-end). Thus our effort introduces a state-of-the-art first model for end-to-end extraction that is already superior to the best prior non end-to-end model for this task. |
Yuhang Jiang; Ramakanth Kavuluru; | arxiv-cs.CL | 2023-03-29 |
| 465 | TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The latest trigger-enhanced methods propose trigger prediction tasks to promote DRE. |
Hao An; Dongsheng Chen; Weiyuan Xu; Zhihong Zhu; Yuexian Zou; | arxiv-cs.CL | 2023-03-29 |
| 466 | Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To better glue the cross-modal semantics therein, we capture hinting features from user comments, which are retrieved via jointly leveraging visual and lingual similarity. |
Chunpu Xu; Jing Li; | arxiv-cs.CL | 2023-03-27 |
| 467 | ReVersion: Diffusion-Based Relation Inversion from Images IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose the Relation Inversion task, which aims to learn a specific relation (represented as relation prompt) from exemplar images. |
Ziqi Huang; Tianxing Wu; Yuming Jiang; Kelvin C. K. Chan; Ziwei Liu; | arxiv-cs.CV | 2023-03-23 |
| 468 | DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we construct a large-scale document-level fine-grained JERE dataset DocRED-FE, which improves DocRED with Fine-Grained Entity Type. |
HONGBO WANG et. al. | arxiv-cs.CL | 2023-03-20 |
| 469 | GCRE-GPT: A Generative Model for Comparative Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that comparative relations can be directly extracted with high accuracy, by generative model. |
Yequan Wang; Hengran Zhang; Aixin Sun; Xuying Meng; | arxiv-cs.CL | 2023-03-15 |
| 470 | Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We perform error analyses and examine how different prompting strategies affect the performance of MRC models. |
CHENG PENG et. al. | arxiv-cs.CL | 2023-03-14 |
| 471 | Dynamic Multi-View Fusion Mechanism For Chinese Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods tend to ignore the internal information of the Chinese character and cannot filter out the noisy information of external knowledge. To address these issues, we propose a mixture-of-view-experts framework (MoVE) to dynamically learn multi-view features for Chinese relation extraction. |
JING YANG et. al. | arxiv-cs.CL | 2023-03-09 |
| 472 | Knowledge-augmented Few-shot Visual Relation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, humans have the ability to learn new relationships with just few examples based on their knowledge. Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD. |
TIANYU YU et. al. | arxiv-cs.CV | 2023-03-09 |
| 473 | Document-level Relation Extraction with Cross-sentence Reasoning Graph IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, existing studies only focus on entity-level reasoning paths without considering global interactions among entities cross-sentence. To these ends, we propose a novel document-level RE model with a GRaph information Aggregation and Cross-sentence Reasoning network (GRACR). |
Hongfei Liu; Zhao Kang; Lizong Zhang; Ling Tian; Fujun Hua; | arxiv-cs.CL | 2023-03-07 |
| 474 | Does Synthetic Data Generation of LLMs Help Clinical Text Mining? IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, their effectiveness in the healthcare sector remains uncertain. In this study, we seek to investigate the potential of ChatGPT to aid in clinical text mining by examining its ability to extract structured information from unstructured healthcare texts, with a focus on biological named entity recognition and relation extraction. |
Ruixiang Tang; Xiaotian Han; Xiaoqian Jiang; Xia Hu; | arxiv-cs.CL | 2023-03-07 |
| 475 | Information Enhancement for Joint Extraction of Entity and Relation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint entity and relation extraction is an important research topic in natural language processing. However, the current work cannot clearly identify the boundaries of entity and … |
Weihu Guo; Shengyang Li; Yunfei Liu; Xipeng Fan; Miaobo Hu; | Proceedings of the 2023 7th International Conference on … | 2023-03-03 |
| 476 | Few-shot Relation Classification Using Clustering-based Prototype Modification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Mingtong Wen; Tingyu Xia; Bowen Liao; Yuan Tian; | Knowl. Based Syst. | 2023-03-01 |
| 477 | Detection of Generative Linguistic Steganography Based on Explicit and Latent Text Word Relation Mining Using Deep Learning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Covert communication channels can be easily constructed using text steganography based on social media. Offenders can easily utilize these channels to engage in various criminal … |
Songbin Li; Jingang Wang; Peng Liu; | IEEE Transactions on Dependable and Secure Computing | 2023-03-01 |
| 478 | Semantic Piecewise Convolutional Neural Network with Adaptive Negative Training for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
MEI YU et. al. | Neurocomputing | 2023-03-01 |
| 479 | Edge-Labeled and Node-Aggregated Graph Neural Networks for Few-Shot Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View |
Jiayi Wang; Lina Yang; Xichun Li; P. Wang; Zuqiang Meng; | Int. J. Pattern Recognit. Artif. Intell. | 2023-02-24 |
| 480 | Relation Extraction Based on Prompt Information and Feature Reuse Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: To alleviate the problem of under-utilization features of sentence-level relation extraction, which leads to insufficient performance of the pre-trained language model and … |
Ping Feng; Xin Zhang; Jian Zhao; Yingying Wang; Biao Huang; | Data Intelligence | 2023-02-22 |
| 481 | Three Heads Better Than One: Pure Entity, Relation Label and Adversarial Training for Cross-domain Few-shot Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In this paper, we study cross-domain relation extraction. Since new data mapping to feature spaces always differs from the previously seen data due to a domain shift, few-shot … |
Wenlong Fang; Chunping Ouyang; Qiang Lin; Yue Yuan; | Data Intelligence | 2023-02-22 |
| 482 | 90% F1 Score in Relational Triple Extraction: Is It Real ? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a benchmark study of state-of-the-art joint entity and relation extraction models under a more realistic setting. |
Pratik Saini; Samiran Pal; Tapas Nayak; Indrajit Bhattacharya; | arxiv-cs.CL | 2023-02-20 |
| 483 | DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations. This work aims at addressing these issues to improve the usage of ER in DocRE. |
Youmi Ma; An Wang; Naoaki Okazaki; | arxiv-cs.CL | 2023-02-16 |
| 484 | LabelPrompt: Effective Prompt-based Learning for Relation Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, associating natural language words that fill the masked token with semantic relation labels (\textit{e.g.} \textit{“org:founded\_by}”) is difficult. To address this challenge, this paper presents a novel prompt-based learning method, namely LabelPrompt, for the relation classification task. |
Wenjie Zhang; Xiaoning Song; Zhenhua Feng; Tianyang Xu; Xiaojun Wu; | arxiv-cs.CL | 2023-02-15 |
| 485 | Metamorphic Testing of Relation Extraction Models Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation extraction (RE) is a fundamental NLP task that aims to identify relations between some entities regarding a given text. RE forms the basis for many advanced NLP tasks, … |
Yuhe Sun; Zuohua Ding; Hongyun Huang; Senhao Zou; Mingyue Jiang; | Algorithms | 2023-02-10 |
| 486 | Event Temporal Relation Extraction with Bayesian Translational Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce Bayesian-Trans, a Bayesian learning-based method that models the temporal relation representations as latent variables and infers their values via Bayesian inference and translational functions. |
Xingwei Tan; Gabriele Pergola; Yulan He; | arxiv-cs.CL | 2023-02-09 |
| 487 | Review of Entity Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out … |
Meimei Tuo; Wenzhong Yang; | J. Intell. Fuzzy Syst. | 2023-02-09 |
| 488 | Image Caption Generation Using Visual Attention Prediction and Contextual Spatial Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Reshmi Sasibhooshan; S. Kumaraswamy; Santhoshkumar Sasidharan; | Journal of Big Data | 2023-02-08 |
| 489 | Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an efficient end-to-end model, the Joint-NER-RE-Fourier (JNRF), to jointly learn the tasks of named entity recognition and relation extraction for documents of variable length. |
Anthony Yazdani; Dimitrios Proios; Hossein Rouhizadeh; Douglas Teodoro; | arxiv-cs.CL | 2023-02-08 |
| 490 | Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks:A Use Case in Adverse Drug Events Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Current approaches for clinical information extraction are inefficient in terms of computational costs and memory consumption, hindering their application to process large-scale … |
A. Yazdani; D. Proios; H. Rouhizadeh; D. Teodoro; | ArXiv | 2023-02-08 |
| 491 | FGSI: Distant Supervision for Relation Extraction Method Based on Fine-Grained Semantic Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose that the key semantic information within a sentence plays a key role in the relationship extraction of entities. |
CHENGHONG SUN et. al. | arxiv-cs.CL | 2023-02-03 |
| 492 | More Than Syntaxes: Investigating Semantics to Zero-shot Cross-lingual Relation Extraction and Event Argument Role Labelling Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Syntactic dependency structures are commonly utilized as language-agnostic features to solve the word order difference issues in zero-shot cross-lingual relation and event … |
KAIWEN WEI et. al. | ACM Transactions on Asian and Low-Resource Language … | 2023-02-01 |
| 493 | Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. |
Kaifeng Gao; Long Chen; Hanwang Zhang; Jun Xiao; Qianru Sun; | arxiv-cs.CV | 2023-02-01 |
| 494 | Document-level Relation Extraction with Two-stage Dynamic Graph Attention Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
QI SUN et. al. | Knowl. Based Syst. | 2023-02-01 |
| 495 | SecureRC: A System for Privacy-preserving Relation Classification Using Secure Multi-party Computation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
Chen Gao; Jia Yu; | Comput. Secur. | 2023-02-01 |
| 496 | RoRED: Bootstrapping Labeling Rule Discovery for Robust Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Wenjun Hou; Liang Hong; Haoshuai Xu; Wei Yin; | Inf. Sci. | 2023-02-01 |
| 497 | Relational Distance and Document-level Contrastive Pre-training Based Relation Extraction Model Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yi Dong; Xiaolong Xu; | Pattern Recognit. Lett. | 2023-02-01 |
| 498 | Weakly-Supervised Questions for Zero-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this method required manually creating gold question templates for each new relation. Here, we do away with these gold templates and instead learn a model that can generate questions for unseen relations. |
Saeed Najafi; Alona Fyshe; | arxiv-cs.CL | 2023-01-21 |
| 499 | Learning Trajectory-Conditioned Relations to Predict Pedestrian Crossing Behavior Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Pedestrian intent, defined as future action, e.g., start crossing, can be dependent on traffic surroundings. In this paper, we develop a framework to incorporate such dependency given observed pedestrian trajectory and scene frames. |
Chen Zhou; Ghassan AlRegib; Armin Parchami; Kunjan Singh; | arxiv-cs.CV | 2023-01-13 |
| 500 | Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we fully utilize the models of all clients and propose a novel concept of \textit{major classifier vectors}, where a group of class vectors is obtained in an ensemble rather than the weighted average method on the server. |
Chunhui Du; Hao He; Yaohui Jin; | arxiv-cs.AI | 2023-01-12 |
| 501 | Multilingual Entity and Relation Extraction from Unified to Language-specific Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages. |
ZIXIANG WANG et. al. | arxiv-cs.CL | 2023-01-11 |
| 502 | API Entity and Relation Joint Extraction from Text Via Dynamic Prompt-tuned Language Model IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast to matching or labeling API entities and relations, this paper formulates heterogeneous API extraction and API relation extraction task as a sequence-to-sequence generation task, and proposes AERJE, an API entity-relation joint extraction model based on the large pre-trained language model. |
QING HUANG et. al. | arxiv-cs.SE | 2023-01-10 |
| 503 | Revisiting Graph Meaning Representations Through Decoupling Contextual Representation Learning and Structural Information Propagation Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research. Despite the … |
Li Zhou; Wenyu Chen; DingYi Zeng; Hong Qu; Daniel Hershcovich; | ArXiv | 2023-01-01 |
| 504 | JoinER-BART: Joint Entity and Relation Extraction With Constrained Decoding, Representation Reuse and Fusion Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint Entity and Relation Extraction (JERE) is an important research direction in Information Extraction (IE). Given the surprising performance with fine-tuning of pre-trained … |
Hongyang Chang; Hongfei Xu; Josef van Genabith; Deyi Xiong; Hongying Zan; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2023-01-01 |
| 505 | Improving Zero-shot Relation Classification Via Automatically-acquired Entailment Templates Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: While fully supervised relation classification (RC) models perform well on large-scale datasets, their performance drops drastically in low-resource settings. As generating … |
Mahdi Rahimi; M. Surdeanu; | Workshop on Representation Learning for NLP | 2023-01-01 |
| 506 | An Angular Shrinkage BERT Model for Few-shot Relation Extraction with None-of-the-above Detection Related Papers Related Patents Related Grants Related Venues Related Experts View |
Junwen Wang; Yongbin Gao; Zhijun Fang; | Pattern Recognit. Lett. | 2023-01-01 |
| 507 | Multi-relation Identification for Few-Shot Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Dazhuang Wang; Shaojuan Wu; Xiaowang Zhang; Zhiyong Feng; | International Conference on Artificial Neural Networks | 2023-01-01 |
| 508 | Generating Labeled Data for Relation Extraction: A Meta Learning Approach with Joint GPT-2 Training Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Relation Extraction (RE) is the task of identifying semantic relation between real-world entities mentioned in text. Despite significant progress in RE research, a remaining … |
Amir Pouran Ben Veyseh; Franck Dernoncourt; Bonan Min; Thien Huu Nguyen; | Annual Meeting of the Association for Computational … | 2023-01-01 |
| 509 | RECESS: Resource for Extracting Cause, Effect, and Signal Spans Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Causality expresses the relation between two arguments, one of which represents the cause and the other the effect (or consequence). Causal relations are fundamental to human … |
FIONA ANTING TAN et. al. | International Joint Conference on Natural Language … | 2023-01-01 |
| 510 | Joint Biomedical Entity and Relation Extraction Based on Feature Filter Table Labeling Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Joint biomedical entity and relation extraction is essential in biomedical text mining. It automatically identifies entities and uncovers the relation between them from biomedical … |
Zhaojie Sun; L. Xing; Longbo Zhang; Hongzhen Cai; Maozu Guo; | IEEE Access | 2023-01-01 |
| 511 | An Entity-Relation Joint Extraction Method Based on Two Independent Sub-Modules From Unstructured Text Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting entity, relation, and attribute information from unstructured text is crucial for constructing large-scale knowledge graphs (KG). Existing research approaches either … |
Su Liu; Wenqi Lyu; Xiao Ma; Jike Ge; | IEEE Access | 2023-01-01 |
| 512 | Fin-EMRC: An Efficient Machine Reading Comprehension Framework for Financial Entity-Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Extracting entities and their relationships from financial documents is crucial for analyzing and predicting future market trends. However, the current state of the art in this … |
Yixuan Chai; Ming Chen; Haipang Wu; Song Wang; | IEEE Access | 2023-01-01 |
| 513 | CoVariance-based Causal Debiasing for Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Lin Ren; Yongbin Liu; Yixin Cao; Chunping Ouyang; | Conference on Empirical Methods in Natural Language … | 2023-01-01 |
| 514 | Continual Relation Extraction Via Linear Mode Connectivity and Interval Cross Training Related Papers Related Patents Related Grants Related Venues Related Experts View |
Qidong Chen; Jun Sun; V. Palade; Zihao Yu; | Knowl. Based Syst. | 2023-01-01 |
| 515 | Adaptive Hinge Balance Loss for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View |
Jize Wang; Xinyi Le; Xiaodi Peng; Cailian Chen; | Conference on Empirical Methods in Natural Language … | 2023-01-01 |
| 516 | A Three-Stage Framework for Event-Event Relation Extraction with Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View |
FENG HUANG et. al. | International Conference on Neural Information Processing | 2023-01-01 |
| 517 | SE-Prompt: Exploring Semantic Enhancement with Prompt Tuning for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Cai Wang; Dongyang Li; Xiaofeng He; | International Conference on Advanced Data Mining and … | 2023-01-01 |
| 518 | Corpus-Based Relation Extraction By Identifying and Refining Relation Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View |
Sizhe Zhou; Suyu Ge; Jiaming Shen; Jiawei Han; | ECML/PKDD | 2023-01-01 |
| 519 | Explore The Way: Exploring Reasoning Path By Bridging Entities for Effective Cross-Document Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Junyoung Son; Jinsung Kim; J. Lim; Yoonna Jang; Heu-Jeoung Lim; | Conference on Empirical Methods in Natural Language … | 2023-01-01 |
| 520 | Position-Aware Attention Mechanism–Based Bi-graph for Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Guiduo Duan; Yunrui Dong; Jiayu Miao; Tianxi Huang; | Cognitive Computation | 2023-01-01 |
| 521 | A Cross-Attention Fusion Based Graph Convolution Auto-Encoder for Open Relation Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Open Relation Extraction (OpenRE) aims at clustering relation instances to extract relation types. By learning relation patterns between named entities, it clusters semantically … |
Bin-Hone Xie; Yu Li; Hongyan Zhao; Lihu Pan; Enhui Wang; | IEEE/ACM Transactions on Audio, Speech, and Language … | 2023-01-01 |
| 522 | DoreBer: Document-Level Relation Extraction Method Based on BernNet Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Document-level relation extraction (RE) task aims to predict predefined relation types of every entity pair in a given document. Compared with the sentence-level counterpart, … |
Boya Yuan; Liwen Xu; | IEEE Access | 2023-01-01 |
| 523 | DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
BO LV et. al. | Annual Meeting of the Association for Computational … | 2023-01-01 |
| 524 | MGCN: Medical Relation Extraction Based on GCN Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: . With the progress of society and the improvement of living standards, people pay more and more attention to personal health, and WITMED (Wise Information Technology of med) has … |
Yongpan Wang; Yong Liu; Jianyi Zhang; | Comput. Informatics | 2023-01-01 |
| 525 | UniER: A Unified and Efficient Entity-Relation Extraction Method with Single-Table Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View |
Sha Liu; Dongsheng Wang; Yue Feng; Miaomiao Zhou; Xuewen Zhu; | Natural Language Processing and Chinese Computing | 2023-01-01 |
| 526 | Reinforcement Learning-based Distant Supervision Relation Extraction for Fault Diagnosis Knowledge Graph Construction Under Industry 4.0 IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View |
C. CHEN et. al. | Adv. Eng. Informatics | 2023-01-01 |
| 527 | Distantly Supervised Document-Level Biomedical Relation Extraction with Neighborhood Knowledge Graphs Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We propose a novel distantly supervised document-level biomedical relation extraction model that uses partial knowledge graphs that include the graph neighborhood of the entities … |
T. Matsubara; Makoto Miwa; Yutaka Sasaki; | Workshop on Biomedical Natural Language Processing | 2023-01-01 |
| 528 | Biomedical Relation Extraction with Entity Type Markers and Relation-specific Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Recently, several methods have tackled the relation extraction task with QA and have shown successful results. However, the effectiveness of existing methods in specific domains, … |
Koshi Yamada; Makoto Miwa; Yutaka Sasaki; | Workshop on Biomedical Natural Language Processing | 2023-01-01 |
| 529 | Ontology-Driven Extraction of Contextualized Information from Research Publications Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: : We present transformer-based methods for extracting information about research processes from scholarly publications. We developed a two-stage pipeline comprising a … |
Vayianos Pertsas; P. Constantopoulos; | International Conference on Knowledge Engineering and … | 2023-01-01 |
| 530 | Powering Fine-Tuning: Learning Compatible and Class-Sensitive Representations for Domain Adaption Few-shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
YIJUN LIU et. al. | International Conference on Database Systems for Advanced … | 2023-01-01 |
| 531 | Temporal Relation Classification Using Boolean Question Answering Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Classifying temporal relations between a pair of events is crucial to natural language understanding and a well-known natural language processing task. Given a document and two … |
O. Cohen; Kfir Bar; | Annual Meeting of the Association for Computational … | 2023-01-01 |
| 532 | Distant Supervision Relation Extraction with Improved PCNN and Multi-level Attention Related Papers Related Patents Related Grants Related Venues Related Experts View |
Yang Zou; Qifei Wang; Zhen Wang; Jian Zhou; Xiaoqin Zeng; | Knowledge Science, Engineering and Management | 2023-01-01 |
| 533 | HIM: An End-to-End Hierarchical Interaction Model for Aspect Sentiment Triplet Extraction Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Aspect Sentiment Triplet Extraction (ASTE) is an emerging task of fine-grained sentiment analysis, which aims to extract aspect terms, associated opinion terms, and sentiment … |
YAXIN LIU et. al. | IEEE/ACM Transactions on Audio, Speech, and Language … | 2023-01-01 |
| 534 | ICA-Proto: Iterative Cross Alignment Prototypical Network for Incremental Few-Shot Relation Classification Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In the task of incremental few-shot relation classification, model performance is always limited by the incompatibility between the base feature embedding space and the novel … |
WANGJIE JIANG et. al. | Findings | 2023-01-01 |
| 535 | Enhancing Ontology Knowledge for Domain-Specific Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Xiong Xiong; Chen Wang; Yunfei Liu; Shengyang Li; | China National Conference on Chinese Computational … | 2023-01-01 |
| 536 | A Distantly-Supervised Relation Extraction Method Based on Selective Gate and Noise Correction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhuowei Chen; Yujia Tian; Lian-xi Wang; Shengyi Jiang; | China National Conference on Chinese Computational … | 2023-01-01 |
| 537 | Learning Discriminative Semantic and Multi-view Context for Domain Adaptive Few-Shot Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
MINGHUI ZHAI et. al. | International Conference on Neural Information Processing | 2023-01-01 |
| 538 | Two-Stage Graph Convolutional Networks for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhiqiang Wang; Yiping Yang; Junjie Ma; | International Conference on Neural Information Processing | 2023-01-01 |
| 539 | Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Zhe Yang; Y. Huang; Junlan Feng; | Annual Meeting of the Association for Computational … | 2023-01-01 |
| 540 | Chain-of-Thought Prompt Distillation for Multimodal Named Entity and Multimodal Relation Extraction IF:3 Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE) necessitate the fundamental reasoning capacity for intricate linguistic and multimodal … |
F. Chen; Yujian Feng; | ArXiv | 2023-01-01 |
| 541 | Semi-Supervised Bootstrapped Syntax-Semantics-Based Approach for Agriculture Relation Extraction for Knowledge Graph Creation and Reasoning Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: We propose a novel approach that uses semi-supervised learning to extract triplets from domain-specific texts and create a Knowledge Graph (KG), with a focus on the agricultural … |
G. Veena; Deepa Gupta; Vani Kanjirangat; | IEEE Access | 2023-01-01 |
| 542 | Bridging Research Fields: An Empirical Study on Joint, Neural Relation Extraction Techniques Related Papers Related Patents Related Grants Related Venues Related Experts View |
Lars Ackermann; Julian Neuberger; Martin Käppel; S. Jablonski; | International Conference on Advanced Information Systems … | 2023-01-01 |
| 543 | A Spectral Viewpoint on Continual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View |
Huy Nguyen; Chien Nguyen; L. Van; A. Luu; Thien Nguyen; | Conference on Empirical Methods in Natural Language … | 2023-01-01 |
| 544 | ReSel: N-ary Relation Extraction from Scientific Text and Tables By Learning to Retrieve and Select IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study the problem of extracting N-ary relation tuples from scientific articles. |
YUCHEN ZHUANG et. al. | emnlp | 2022-12-30 |
| 545 | Multilingual Relation Classification Via Efficient and Effective Prompting IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the first work on prompt-based multilingual relation classification (RC), by introducing an efficient and effective method that constructs prompts from relation triples and involves only minimal translation for the class labels. |
Yuxuan Chen; David Harbecke; Leonhard Hennig; | emnlp | 2022-12-30 |
| 546 | Learning Robust Representations for Continual Relation Extraction Via Adversarial Class Augmentation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Most previous work attributes catastrophic forgetting to the corruption of the learned representations as new relations come, with an implicit assumption that the CRE models have adequately learned the old relations. In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process. |
PEIYI WANG et. al. | emnlp | 2022-12-30 |
| 547 | Boosting Document-Level Relation Extraction By Mining and Injecting Logical Rules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MILR, a logic enhanced framework that boosts DocRE by Mining and Injecting Logical Rules. |
Shengda Fan; Shasha Mo; Jianwei Niu; | emnlp | 2022-12-30 |
| 548 | Entity-centered Cross-document Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, they utilize all the text paths in a document bag in a coarse-grained way, without considering the connections between these text paths. In this paper, we aim to address both of these shortages and push the state-of-the-art for cross-document RE. |
FENGQI WANG et. al. | emnlp | 2022-12-30 |
| 549 | Fine-grained Contrastive Learning for Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose fine-grained contrastive learning (FineCL) for RE, which leverages fine-grained information about which silver labels are and are not noisy to improve the quality of learned relationship representations for RE. |
William Hogan; Jiacheng Li; Jingbo Shang; | emnlp | 2022-12-30 |
| 550 | Large Language Models Are Few-shot Clinical Information Extractors IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that large language models, such as InstructGPT (Ouyang et al., 2022), perform well at zero- and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain. |
Monica Agrawal; Stefan Hegselmann; Hunter Lang; Yoon Kim; David Sontag; | emnlp | 2022-12-30 |
| 551 | Revisiting DocRED – Addressing The False Negative Problem in Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we find that the annotation of DocRED is incomplete, i.e., false negative samples are prevalent. We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset. |
Qingyu Tan; Lu Xu; Lidong Bing; Hwee Tou Ng; Sharifah Mahani Aljunied; | emnlp | 2022-12-30 |
| 552 | Query-based Instance Discrimination Network for Relational Triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they still suffer from error propagation, relation redundancy and lack of high-level connections between triples. To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples. |
ZEQI TAN et. al. | emnlp | 2022-12-30 |
| 553 | A Dataset for Hyper-Relational Extraction and A Cube-Filling Approach IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers. |
Yew Ken Chia; Lidong Bing; Sharifah Mahani Aljunied; Luo Si; Soujanya Poria; | emnlp | 2022-12-30 |
| 554 | MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few pre-defined relational instances as well as mine the specific meanings for cluster interpretability. |
JIAXIN WANG et. al. | emnlp | 2022-12-30 |
| 555 | A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To solve the common incomplete labeling problem, we propose a unified positive-unlabeled learning framework – shift and squared ranking loss positive-unlabeled (SSR-PU) learning. |
Ye Wang; Xinxin Liu; Wenxin Hu; Tao Zhang; | emnlp | 2022-12-30 |
| 556 | Graph-based Model Generation for Few-Shot Relation Extraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing models follow a ?one-for-all? scheme where one general large model performs all individual N-way-K-shot tasks in FSRE, which prevents the model from achieving the optimal point on each task. In view of this, we propose a model generation framework that consists of one general model for all tasks and many tiny task-specific models for each individual task. |
Wanli Li; Tieyun Qian; | emnlp | 2022-12-30 |
| 557 | Better Few-Shot Relation Extraction with Label Prompt Dropout IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead, we present a novel approach called label prompt dropout, which randomly removes label descriptions in the learning process. |
Peiyuan Zhang; Wei Lu; | emnlp | 2022-12-30 |
| 558 | Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we introduce cross-stitch bi-encoders, which allow full interaction between the text encoder and the KG encoder via a cross-stitch mechanism. |
Qin Dai; Benjamin Heinzerling; Kentaro Inui; | emnlp | 2022-12-30 |
| 559 | RelU-Net: Syntax-aware Graph U-Net for Relational Triple Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is due to the absence of entity locations, which is the prerequisite for pruning noisy edges from the dependency tree, when extracting relational triples. In this paper, we propose a unified framework to tackle this challenge and incorporate syntactic information for relational triple extraction. |
Yunqi Zhang; Yubo Chen; Yongfeng Huang; | emnlp | 2022-12-30 |
| 560 | Towards Relation Extraction from Speech IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new listening information extraction task, i. e. , speech relation extraction. |
TONGTONG WU et. al. | emnlp | 2022-12-30 |
| 561 | Open Relation and Event Type Discovery with Type Abstraction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This calls for systems that can automatically infer new types from given corpora, a task which we refer to as type discovery. To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type.</ |