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Paper Digest: Most Cited Papers on Relation Extraction

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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 impact.

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TABLE 1: Paper Digest: Most Cited Papers on Relation Extraction

Paper Author(s) Source Date
1 Distant Supervision For Relation Extraction Without Labeled Data
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 Mike Mintz; Steven Bills; Rion Snow; Daniel Jurafsky; acl 2009-12-30
2 Knowledge Graph Embedding: A Survey of Approaches and Applications
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Abstract: Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving …		 Quan Wang; Zhendong Mao; Bin Wang; Li Guo; IEEE Transactions on Knowledge and Data Engineering 2017-01-01
3 Attention-Based Bidirectional Long Short-Term Memory Networks For Relation Classification
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 PENG ZHOU et. al. acl 2016-12-30
4 Modeling Relations and Their Mentions Without Labeled Text
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Abstract: Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they …		 Sebastian Riedel; Limin Yao; Andrew McCallum; 2010-01-01
5 Kernel Methods For Relation Extraction
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 Dmitry Zelenko; Chinatsu Aone; Anthony Richardella; emnlp 2002-12-30
6 End-to-End Relation Extraction Using LSTMs On Sequences And Tree Structures
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 Makoto Miwa; Mohit Bansal; acl 2016-12-30
7 A Shortest Path Dependency Kernel For Relation Extraction
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 Razvan Bunescu; Raymond Mooney; emnlp 2005-12-30
8 Distant Supervision For Relation Extraction Via Piecewise Convolutional Neural Networks
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 Daojian Zeng; Kang Liu; Yubo Chen; Jun Zhao; emnlp 2015-12-30
9 Neural Relation Extraction With Selective Attention Over Instances
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 Yankai Lin; Shiqi Shen; Zhiyuan Liu; Huanbo Luan; Maosong Sun; acl 2016-12-30
10 Dependency Tree Kernels For Relation Extraction
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 Aron Culotta; Jeffrey Sorensen; acl 2004-12-30
11 Multimodal Named Entity Recognition and Relation Extraction with Retrieval-Augmented Strategy
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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
12 Position-aware Attention And Supervised Data Improve Slot Filling
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Highlight: This paper simultaneously addresses two issues that have held back prior work.		 Yuhao Zhang; Victor Zhong; Danqi Chen; Gabor Angeli; Christopher D. Manning; emnlp 2017-11-07
13 Exploring Various Knowledge In Relation Extraction
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 GuoDong Zhou; Jian Su; Jie Zhang; Min Zhang; acl 2005-12-30
14 Multi-instance Multi-label Learning For Relation Extraction
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 Mihai Surdeanu; Julie Tibshirani; Ramesh Nallapati; Christopher D. Manning; emnlp 2012-12-30
15 Matching The Blanks: Distributional Similarity For Relation Learning
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Highlight: In this paper, we build on extensions of Harris’ distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text.		 Livio Baldini Soares; Nicholas FitzGerald; Jeffrey Ling; Tom Kwiatkowski; arxiv-cs.CL 2019-06-07
16 Matching The Blanks: Distributional Similarity For Relation Learning
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Highlight: In this paper, we build on extensions of Harris’ distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text.		 Livio Baldini Soares; Nicholas FitzGerald; Jeffrey Ling; Tom Kwiatkowski,; acl 2019-07-28
17 Graph Convolution Over Pruned Dependency Trees Improves Relation Extraction
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Highlight: We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel.		 Yuhao Zhang; Peng Qi; Christopher D. Manning; emnlp 2018-11-02
18 Relation Extraction With Matrix Factorization And Universal Schemas
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 Sebastian Riedel; Limin Yao; Andrew McCallum; Benjamin M. Marlin; naacl 2013-12-30
19 FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset With State-of-the-Art Evaluation
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Highlight: We present a Few-Shot Relation Classification Dataset (dataset), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers.		 XU HAN et. al. emnlp 2018-11-02
20 Classifying Relations By Ranking With Convolutional Neural Networks
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Highlight: In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN).		 Cicero Nogueira dos Santos; Bing Xiang; Bowen Zhou; arxiv-cs.CL 2015-04-24
21 Entity, Relation, And Event Extraction With Contextualized Span Representations
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Highlight: We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.		 David Wadden; Ulme Wennberg; Yi Luan; Hannaneh Hajishirzi; emnlp 2019-11-02
22 Subsequence Kernels For Relation Extraction
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 Raymond J. Mooney; Razvan C. Bunescu; nips 2005-12-30
23 Visual Translation Embedding Network For Visual Relation Detection
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Highlight: We propose a novel feature extraction layer that enables object-relation knowledge transfer in a fully-convolutional fashion that supports training and inference in a single forward/backward pass.		 Hanwang Zhang; Zawlin Kyaw; Shih-Fu Chang; Tat-Seng Chua; cvpr 2017-06-17
24 Fine-Grained Entity Recognition
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Highlight: This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes an unsupervised method for collecting training data, and presents the FIGER implementation.		 Xiao Ling; Daniel S. Weld; aaai 2012-12-30
25 Cross-Sentence N-ary Relation Extraction With Graph LSTMs
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Highlight: In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction.		 Nanyun Peng; Hoifung Poon; Chris Quirk; Kristina Toutanova; Wen-tau Yih; tacl 2017-12-30
26 Relation Classification Via Multi-Level Attention CNNs
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 Linlin Wang; Zhu Cao; Gerard de Melo; Zhiyuan Liu; acl 2016-12-30
27 Simple BERT Models For Relation Extraction And Semantic Role Labeling
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Highlight: We present simple BERT-based models for relation extraction and semantic role labeling.		 Peng Shi; Jimmy Lin; arxiv-cs.CL 2019-04-10
28 DocRED: A Large-Scale Document-Level Relation Extraction Dataset
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Highlight: In order to acceleratethe research on document-level RE, we in-troduce DocRED, a new dataset constructedfrom Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE fromplain text; (2) DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document; (3) alongwith the human-annotated data, we also offer large-scale distantly supervised data, whichenables DocRED to be adopted for both supervised and weakly supervised scenarios.		 YUAN YAO et. al. acl 2019-07-28
29 DocRED: A Large-Scale Document-Level Relation Extraction Dataset
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Highlight: In order to verify the challenges of document-level RE, we implement recent state-of-the-art methods for RE and conduct a thorough evaluation of these methods on DocRED.		 YUAN YAO et. al. arxiv-cs.CL 2019-06-14
30 The Tradeoffs Between Open And Traditional Relation Extraction
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 Michele Banko; Oren Etzioni; acl 2008-12-30
31 Enriching Pre-trained Language Model With Entity Information For Relation Classification
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Highlight: In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task.		 Shanchan Wu; Yifan He; cikm 2019-11-03
32 Attention Guided Graph Convolutional Networks For Relation Extraction
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Highlight: In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs.		 Zhijiang Guo; Yan Zhang; Wei Lu; arxiv-cs.CL 2019-06-18
33 Attention Guided Graph Convolutional Networks For Relation Extraction
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Highlight: In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs.		 Zhijiang Guo; Yan Zhang; Wei Lu,; acl 2019-07-28
34 KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
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Highlight: To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).		 XIANG CHEN et. al. arxiv-cs.CL 2021-04-15
35 Distant Supervision For Relation Extraction With Sentence-Level Attention And Entity Descriptions
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Highlight: In this paper, we propose a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases.		 Guoliang Ji; Kang Liu; Shizhu He; Jun Zhao; aaai 2017-02-04
36 A Frustratingly Easy Approach for Entity and Relation Extraction
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Highlight: In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders.		 Zexuan Zhong; Danqi Chen; naacl 2021-05-23
37 Span-based Joint Entity and Relation Extraction with Transformer Pre-training
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Highlight: We introduce SpERT, an attention model for span-based joint entity and relation extraction.		 Markus Eberts; Adrian Ulges; arxiv-cs.CL 2019-09-17
38 Joint Entity Recognition And Relation Extraction As A Multi-head Selection Problem
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Highlight: In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool.		 Giannis Bekoulis; Johannes Deleu; Thomas Demeester; Chris Develder; arxiv-cs.CL 2018-04-20
39 Hybrid Attention-Based Prototypical Networks For Noisy Few-Shot Relation Classification
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Highlight: In this paper, we propose hybrid attention-based prototypical networks for the problem of noisy few-shot RC.		 Tianyu Gao; Xu Han; Zhiyuan Liu; Maosong Sun; aaai 2019-01-27
40 Relation Classification Via Recurrent Neural Network
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Highlight: In this paper, we propose a simple framework based on recurrent neural networks (RNN) and compare it with CNN-based model.		 Dongxu Zhang; Dong Wang; arxiv-cs.CL 2015-08-05
41 Entity-Relation Extraction As Multi-Turn Question Answering
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Highlight: In this paper, we propose a new paradigm for the task of entity-relation extraction.		 XIAOYA LI et. al. arxiv-cs.CL 2019-05-14
42 Entity-Relation Extraction As Multi-Turn Question Answering
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Highlight: In this paper, we propose a new paradigm for the task of entity-relation extraction.		 XIAOYA LI et. al. acl 2019-07-28
43 Reinforcement Learning For Relation Classification From Noisy Data
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Highlight: In this paper, we propose a novel model for relation classification at the sentence level from noisy data.		 Jun Feng; Minlie Huang; Li Zhao; Yang Yang; Xiaoyan Zhu; arxiv-cs.IR 2018-08-24
44 GraphRel: Modeling Text As Relational Graphs For Joint Entity And Relation Extraction
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Highlight: In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations.		 Tsu-Jui Fu; Peng-Hsuan Li; Wei-Yun Ma,; acl 2019-07-28
45 Question Answering On Freebase Via Relation Extraction And Textual Evidence
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 Kun Xu; Siva Reddy; Yansong Feng; Songfang Huang; Dongyan Zhao; acl 2016-12-30
46 TPLinker: Single-stage Joint Extraction Of Entities And Relations Through Token Pair Linking
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Highlight: To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias.		 YUCHENG WANG et. al. arxiv-cs.CL 2020-10-26
47 StatSnowball: A Statistical Approach To Extracting Entity Relationships
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Highlight: In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE.		 Jun Zhu; Zaiqing Nie; Xiaojiang Liu; Bo Zhang; Ji-Rong Wen; www 2009-05-13
48 Modeling Joint Entity And Relation Extraction With Table Representation
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 Makoto Miwa; Yutaka Sasaki; emnlp 2014-12-30
49 Large Language Models Are Few-shot Clinical Information Extractors
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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
50 CoType: Joint Extraction Of Typed Entities And Relations With Knowledge Bases
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Highlight: In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision).		 XIANG REN et. al. www 2017-05-13
51 Semantic Relation Classification Via Convolutional Neural Networks With Simple Negative Sampling
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 Kun Xu; Yansong Feng; Songfang Huang; Dongyan Zhao; emnlp 2015-12-30
52 Improved Neural Relation Detection For Knowledge Base Question Answering
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Highlight: In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question.		 MO YU et. al. acl 2017-07-30
53 Structured Prediction As Translation Between Augmented Natural Languages
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Highlight: We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.		 GIOVANNI PAOLINI et. al. arxiv-cs.LG 2021-01-14
54 Reasoning With Latent Structure Refinement For Document-Level Relation Extraction
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Highlight: Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph.		 Guoshun Nan; Zhijiang Guo; Ivan Sekulić; Wei Lu; arxiv-cs.CL 2020-05-13
55 Reasoning With Latent Structure Refinement For Document-Level Relation Extraction
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Highlight: Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph.		 Guoshun Nan; Zhijiang Guo; Ivan Sekulic; Wei Lu; acl 2020-06-20
56 Distant Supervision For Relation Extraction With An Incomplete Knowledge Base
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 Bonan Min; Ralph Grishman; Li Wan; Chang Wang; David Gondek; naacl 2013-12-30
57 Injecting Logical Background Knowledge Into Embeddings For Relation Extraction
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 Tim Rocktäschel; Sameer Singh; Sebastian Riedel; naacl 2015-12-30
58 Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
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Highlight: In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems.		 Wenxuan Zhou; Kevin Huang; Tengyu Ma; Jing Huang; aaai 2021-02-09
59 Simultaneously Self-Attending To All Mentions For Full-Abstract Biological Relation Extraction
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Highlight: In response, we propose a model which simultaneously predicts relationships between all mention pairs in a document. We also introduce a new dataset an order of magnitude larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives.		 Patrick Verga; Emma Strubell; Andrew McCallum; naacl 2018-11-02
60 Exploiting Syntactico-Semantic Structures For Relation Extraction
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 Yee Seng Chan; Dan Roth; acl 2011-12-30
61 Connecting Language And Knowledge Bases With Embedding Models For Relation Extraction
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 Jason Weston; Antoine Bordes; Oksana Yakhnenko; Nicolas Usunier; emnlp 2013-12-30
62 Extraction of Semantic Biomedical Relations from Text Using Conditional Random Fields
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Abstract: BackgroundThe increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of …		 Markus Bundschus; Mathäus Dejori; Martin Stetter; Volker Tresp; Hans-Peter Kriegel; BMC Bioinformatics 2007-01-01
63 FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
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Highlight: We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances?		 TIANYU GAO et. al. emnlp 2019-11-02
64 A Dependency-Based Neural Network For Relation Classification
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 YANG LIU et. al. acl 2015-12-30
65 Distant Supervision For Relation Extraction Beyond The Sentence Boundary
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Highlight: In this paper, we propose the first approach for applying distant supervision to cross- sentence relation extraction.		 Chris Quirk; Hoifung Poon; arxiv-cs.CL 2016-09-15
66 RESIDE: Improving Distantly-Supervised Neural Relation Extraction Using Side Information
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Highlight: In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction.		 Shikhar Vashishth; Rishabh Joshi; Sai Suman Prayaga; Chiranjib Bhattacharyya; Partha Talukdar; arxiv-cs.CL 2018-12-11
67 Tree Kernel-Based Relation Extraction With Context-Sensitive Structured Parse Tree Information
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 GuoDong Zhou; Min Zhang; Dong Hong Ji; QiaoMing Zhu; emnlp 2007-12-30
68 When Are Tree Structures Necessary For Deep Learning Of Representations?
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Highlight: In this paper we benchmark {\bf recursive} neural models against sequential {\bf recurrent} neural models (simple recurrent and LSTM models), enforcing apples-to-apples comparison as much as possible.		 Jiwei Li; Minh-Thang Luong; Dan Jurafsky; Eudard Hovy; arxiv-cs.AI 2015-02-28
69 Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
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Highlight: In this paper, we propose two approaches to use encoder-decoder architecture for jointly extracting entities and relations.		 Tapas Nayak; Hwee Tou Ng; aaai 2020-02-07
70 Unified Named Entity Recognition As Word-Word Relation Classification
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Highlight: In this work, we present a novel alternative by modeling the unified NER as word-word relation classification, namely W^2NER.		 JINGYE LI et. al. aaai 2022-02-07
71 Reducing Wrong Labels In Distant Supervision For Relation Extraction
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 Shingo Takamatsu; Issei Sato; Hiroshi Nakagawa; acl 2012-12-30
72 A Hierarchical Framework For Relation Extraction With Reinforcement Learning
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Highlight: This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation.		 Ryuichi Takanobu; Tianyang Zhang; Jiexi Liu; Minlie Huang; aaai 2019-01-27
73 Two Are Better Than One: Joint Entity And Relation Extraction With Table-Sequence Encoders
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Highlight: In this work, we propose the novel table-sequence encoders where two different encoders – a table encoder and a sequence encoder are designed to help each other in the representation learning process.		 Jue Wang; Wei Lu; emnlp 2020-11-12
74 Joint Entity and Relation Extraction Based on A Hybrid Neural Network
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 SUNCONG ZHENG et. al. Neurocomputing 2017-01-01
75 Double Graph Based Reasoning For Document-level Relation Extraction
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Highlight: In this paper, we propose Graph Aggregation-and-Inference Network (GAIN), a method to recognize such relations for long paragraphs.		 Shuang Zeng; Runxin Xu; Baobao Chang; Lei Li; emnlp 2020-11-12
76 Improved Relation Classification By Deep Recurrent Neural Networks With Data Augmentation
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Highlight: In this paper, we propose deep recurrent neural networks (DRNNs) for relation classification to tackle this challenge.		 YAN XU et. al. arxiv-cs.CL 2016-01-14
77 RelExt: A Tool for Relation Extraction from Text in Ontology Extension
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Abstract: Domain ontologies very rarely model verbs as relations holding between concepts. However, the role of the verb as a central connecting element between concepts is undeniable. …		 Alexander Schutz; Paul Buitelaar; 2005-01-01
78 Adversarial Training For Relation Extraction
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Highlight: We apply adversarial training in relation extraction within the multi-instance multi-label learning framework.		 Yi Wu; David Bamman; Stuart Russell; emnlp 2017-11-07
79 Robust Distant Supervision Relation Extraction Via Deep Reinforcement Learning
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Highlight: To do this, our paper describes a radical solution-We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information.		 Pengda Qin; Weiran Xu; William Yang Wang; acl 2018-07-15
80 A Systematic Exploration Of The Feature Space For Relation Extraction
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 Jing Jiang; ChengXiang Zhai; naacl 2007-12-30
81 Scientific and Statistical Database Management, 21st International Conference, SSDBM 2009, New Orleans, LA, USA, June 2-4, 2009, Proceedings
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Abstract: Invited Presentation.- The Scientific Data Management Center: Providing Technologies for Large Scale Scientific Exploration.- Improving the End-User Experience.- Query …		 Marianne Winslett; 2009-01-01
82 Bidirectional Recurrent Convolutional Neural Network For Relation Classification
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 Rui Cai; Xiaodong Zhang; Houfeng Wang; acl 2016-12-30
83 Typed Tensor Decomposition Of Knowledge Bases For Relation Extraction
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 Kai-Wei Chang; Wen-tau Yih; Bishan Yang; Christopher Meek; emnlp 2014-12-30
84 Learning From Context Or Names? An Empirical Study On Neural Relation Extraction
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Highlight: Based on the analyses, we propose an entity-masked contrastive pre-training framework for RE to gain a deeper understanding on both textual context and type information while avoiding rote memorization of entities or use of superficial cues in mentions.		 HAO PENG et. al. arxiv-cs.CL 2020-10-05
85 Pose-Aware Multi-Level Feature Network for Human Object Interaction Detection
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Highlight: To address those challenges, we propose a multi-level relation detection strategy that utilizes human pose cues to capture global spatial configurations of relations and as an attention mechanism to dynamically zoom into relevant regions at human part level.		 Bo Wan; Desen Zhou; Yongfei Liu; Rongjie Li; Xuming He; iccv 2019-10-24
86 Question Analysis: How Watson Reads A Clue
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Abstract: The first stage of processing in the IBM Watson™ system is to perform a detailed analysis of the question in order to determine what it is asking for and how best to approach …		 ADAM LALLY et. al. IBM J. Res. Dev. 2012-01-01
87 Inter-sentence Relation Extraction With Document-level Graph Convolutional Neural Network
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Highlight: We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph.		 Sunil Kumar Sahu; Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou,; acl 2019-07-28
88 Inter-sentence Relation Extraction With Document-level Graph Convolutional Neural Network
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Highlight: We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph.		 Sunil Kumar Sahu; Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou; arxiv-cs.CL 2019-06-11
89 Long-tail Relation Extraction Via Knowledge Graph Embeddings And Graph Convolution Networks
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Highlight: We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings.		 NINGYU ZHANG et. al. naacl 2019-06-02
90 Long-tail Relation Extraction Via Knowledge Graph Embeddings And Graph Convolution Networks
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Highlight: We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings.		 NINGYU ZHANG et. al. arxiv-cs.IR 2019-03-04
91 Connecting The Dots: Document-level Neural Relation Extraction With Edge-oriented Graphs
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Highlight: We thus propose an edge-oriented graph neural model for document-level relation extraction.		 Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou; emnlp 2019-11-02
92 Combining Recurrent And Convolutional Neural Networks For Relation Classification
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 Ngoc Thang Vu; Heike Adel; Pankaj Gupta; Hinrich Schütze; naacl 2016-12-30
93 Combining Recurrent And Convolutional Neural Networks For Relation Classification
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Highlight: We present a new context representation for convolutional neural networks for relation classification (extended middle context).		 Ngoc Thang Vu; Heike Adel; Pankaj Gupta; Hinrich Schütze; arxiv-cs.CL 2016-05-24
94 Extraction of Gene-Disease Relations from Medline Using Domain Dictionaries and Machine Learning
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Abstract: We describe a system that extracts disease-gene relations from Medline. We constructed a dictionary for disease and gene names from six public databases and extracted relation …		 HONG-WOO CHUN et. al. Pacific Symposium on Biocomputing. Pacific Symposium on … 2006-01-01
95 Improved Relation Extraction With Feature-Rich Compositional Embedding Models
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 Matthew R. Gormley; Mo Yu; Mark Dredze; emnlp 2015-12-30
96 Document-level Relation Extraction As Semantic Segmentation
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Highlight: Herein, we propose a Document U-shaped Network for document-level relation extraction.		 NINGYU ZHANG et. al. arxiv-cs.CL 2021-06-07
97 Information Extraction from Text
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Abstract: Information extraction is the task of finding structured information from unstructured or semi-structured text. It is an important task in text mining and has been extensively …		 Jing Jiang; 2012-01-01
98 Adversarial Training For Multi-context Joint Entity And Relation Extraction
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Highlight: We show how to use AT for the tasks of entity recognition and relation extraction.		 Giannis Bekoulis; Johannes Deleu; Thomas Demeester; Chris Develder; arxiv-cs.CL 2018-08-21
99 Few-shot Text Classification With Distributional Signatures
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Highlight: In this paper, we explore meta-learning for few-shot text classification.		 Yujia Bao; Menghua Wu; Shiyu Chang; Regina Barzilay; arxiv-cs.CL 2019-08-16
100 End-to-End Neural Relation Extraction With Global Optimization
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Highlight: We build a globally optimized neural model for end-to-end relation extraction, proposing novel LSTM features in order to better learn context representations.		 Meishan Zhang; Yue Zhang; Guohong Fu; emnlp 2017-11-07
101 N-ary Relation Extraction Using Graph-State LSTM
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Highlight: We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing.		 Linfeng Song; Yue Zhang; Zhiguo Wang; Daniel Gildea; emnlp 2018-11-02
102 Combining Distant And Partial Supervision For Relation Extraction
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 Gabor Angeli; Julie Tibshirani; Jean Wu; Christopher D. Manning; emnlp 2014-12-30
103 Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
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Highlight: In this paper, we transform ASTE task into a multi-turn machine reading comprehension (MTMRC) task and propose a bidirectional MRC (BMRC) framework to address this challenge.		 Shaowei Chen; Yu Wang; Jie Liu; Yuelin Wang; arxiv-cs.CL 2021-03-13
104 Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
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Highlight: In this work, we formulate such structure as distinctive dependencies between mention pairs.		 Benfeng Xu; Quan Wang; Yajuan Lyu; Yong Zhu; Zhendong Mao; arxiv-cs.CL 2021-02-19
105 Convolution Neural Network for Relation Extraction
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Abstract: Deep Neural Network has been applied to many Natural Language Processing tasks. Instead of building hand-craft features, DNN builds features by automatic learning, fitting …		 Chunyang Liu; Wenbo Sun; Wen-Han Chao; Wanxiang Che; 2013-01-01
106 A Comparison of Knowledge Extraction Tools for The Semantic Web
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Abstract: In the last years, basic NLP tasks: NER, WSD, relation extraction, etc. have been configured for Semantic Web tasks including ontology learning, linked data population, entity …		 Aldo Gangemi; 2013-01-01
107 Simple Algorithms For Complex Relation Extraction With Applications To Biomedical IE
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 RYAN MCDONALD et. al. acl 2005-12-30
108 Collective Cross-Document Relation Extraction Without Labelled Data
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 Limin Yao; Sebastian Riedel; Andrew McCallum; emnlp 2010-12-30
109 Training Classifiers With Natural Language Explanations
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Highlight: In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision.		 BRADEN HANCOCK et. al. arxiv-cs.CL 2018-05-10
110 DSGAN: Generative Adversarial Training For Distant Supervision Relation Extraction
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Highlight: In this paper, we introduce an adversarial learning framework, which we named DSGAN, to learn a sentence-level true-positive generator.		 Pengda Qin; Weiran Xu; William Yang Wang; acl 2018-07-15
111 Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
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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
112 RTFN: A Robust Temporal Feature Network For Time Series Classification
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Highlight: To this end, we propose a novel robust temporal feature network (RTFN) for feature extraction in time series classification, containing a temporal feature network (TFN) and an LSTM-based attention network (LSTMaN).		 ZHIWEN XIAO et. al. arxiv-cs.LG 2020-11-23
113 Neural Knowledge Acquisition Via Mutual Attention Between Knowledge Graph And Text
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Highlight: We propose a general joint representation learning framework for knowledge acquisition (KA) on two tasks, knowledge graph completion (KGC) and relation extraction (RE) from text.		 Xu Han; Zhiyuan Liu; Maosong Sun; aaai 2018-02-02
114 Video Visual Relation Detection
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Abstract: As a bridge to connect vision and language, visual relations between objects in the form of relation triplet $łangle subject,predicate,object\rangle$, such as person-touch-dog” …		 Xindi Shang; Tongwei Ren; Jingfan Guo; Hanwang Zhang; Tat-Seng Chua; Proceedings of the 25th ACM international conference on … 2017-01-01
115 Parallel Integer Relation Detection: Techniques and Applications
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Abstract: Let {x1,x2,···,xn} be a vector of real numbers. An integer relation algorithm is a computational scheme to find the n integers ak, if they exist, such that a1x1 + a2x2 + ··· + …		 David H. Bailey; David John Broadhurst; Math. Comput. 2001-01-01
116 TACRED Revisited: A Thorough Evaluation Of The TACRED Relation Extraction Task
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Highlight: In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for improvement?		 Christoph Alt; Aleksandra Gabryszak; Leonhard Hennig; acl 2020-06-20
117 Semantic Relation Extraction Using Sequential and Tree-structured LSTM with Attention
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 Zhiqiang Geng; GuoFei Chen; Yongming Han; Gang Lu; Fang Li; Inf. Sci. 2020-01-01
118 OpenNRE: An Open And Extensible Toolkit For Neural Relation Extraction
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Highlight: OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE).		 XU HAN et. al. arxiv-cs.CL 2019-09-28
119 Context-Aware Representations For Knowledge Base Relation Extraction
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Highlight: We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation.		 Daniil Sorokin; Iryna Gurevych; emnlp 2017-11-07
120 Document-Level N-ary Relation Extraction With Multiscale Representation Learning
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Highlight: In this paper, we propose a novel multiscale neural architecture for document-level n-ary relation extraction.		 Robin Jia; Cliff Wong; Hoifung Poon,; naacl 2019-06-02
121 Exploiting Constituent Dependencies for Tree Kernel-Based Semantic Relation Extraction
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Abstract: This paper proposes a new approach to dynamically determine the tree span for tree kernel-based semantic relation extraction. It exploits constituent dependencies to keep the …		 Longhua Qian; Guodong Zhou; Fang Kong; Qiaoming Zhu; Peide Qian; 2008-01-01
122 PPR-FCN: Weakly Supervised Visual Relation Detection Via Parallel Pairwise R-FCN
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Highlight: We aim to tackle a novel vision task called Weakly Supervised Visual Relation Detection (WSVRD) to detect subject-predicate-object relations in an image with object relation groundtruths available only at the image level.		 Hanwang Zhang; Zawlin Kyaw; Jinyang Yu; Shih-Fu Chang; arxiv-cs.CV 2017-08-06
123 A Soft-label Method For Noise-tolerant Distantly Supervised Relation Extraction
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Highlight: To this end, we introduce an entity-pair level denoise method which exploits semantic information from correctly labeled entity pairs to correct wrong labels dynamically during training.		 Tianyu Liu; Kexiang Wang; Baobao Chang; Zhifang Sui; emnlp 2017-11-07
124 Semi-supervised Relation Extraction With Large-scale Word Clustering
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 Ang Sun; Ralph Grishman; Satoshi Sekine; acl 2011-12-30
125 Convolution Kernels On Constituent, Dependency And Sequential Structures For Relation Extraction
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 Truc-Vien T. Nguyen; Alessandro Moschitti; Giuseppe Riccardi; emnlp 2009-12-30
126 Knowledge-oriented Convolutional Neural Network for Causal Relation Extraction from Natural Language Texts
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 Pengfei Li; Kezhi Mao; Expert Syst. Appl. 2019-01-01
127 More Data, More Relations, More Context And More Openness: A Review And Outlook For Relation Extraction
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Highlight: In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE.		 XU HAN et. al. arxiv-cs.CL 2020-04-07
128 Revisiting LSTM Networks For Semi-Supervised Text Classification Via Mixed Objective Function
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Highlight: In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches.		 Devendra Singh Sachan; Manzil Zaheer; Ruslan Salakhutdinov; arxiv-cs.CL 2020-09-08
129 Multi-Level Matching And Aggregation Network For Few-Shot Relation Classification
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Highlight: This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification.		 Zhi-Xiu Ye; Zhen-Hua Ling,; acl 2019-07-28
130 Multi-Level Matching And Aggregation Network For Few-Shot Relation Classification
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Highlight: This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification.		 Zhi-Xiu Ye; Zhen-Hua Ling; arxiv-cs.CL 2019-06-16
131 Embedding Semantic Similarity In Tree Kernels For Domain Adaptation Of Relation Extraction
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 Barbara Plank; Alessandro Moschitti; acl 2013-12-30
132 Joint Entity And Relation Extraction With Set Prediction Networks
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Highlight: To solve this set prediction problem, we propose networks featured by transformers with non-autoregressive parallel decoding.		 DIANBO SUI et. al. arxiv-cs.CL 2020-11-03
133 Dialogue-Based Relation Extraction
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Highlight: We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue.		 Dian Yu; Kai Sun; Claire Cardie; Dong Yu; acl 2020-06-20
134 An Improved Baseline for Sentence-level Relation Extraction
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Highlight: In this paper, we revisit two problems that affect the performance of existing RE models, namely entity representation and noisy or ill-defined labels.		 Wenxuan Zhou; Muhao Chen; arxiv-cs.CL 2021-02-02
135 Joint Extraction Of Entities And Overlapping Relations Using Position-Attentive Sequence Labeling
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Highlight: In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that have relationship with the former.		 DAI DAI et. al. aaai 2019-01-27
136 Sentence Embedding Alignment For Lifelong Relation Extraction
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Highlight: Specifically, we utilize an explicit alignment model to mitigate the sentence embedding distortion of the learned model when training on new data and new relations.		 HONG WANG et. al. arxiv-cs.CL 2019-03-06
137 Sentence Embedding Alignment For Lifelong Relation Extraction
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Highlight: Specifically, we utilize an explicit alignment model to mitigate the sentence embedding distortion of learned model when training on new data and new relations.		 HONG WANG et. al. naacl 2019-06-02
138 Incorporating Lexical Semantic Similarity to Tree Kernel-Based Chinese Relation Extraction
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Abstract: Lexical semantic information plays an important role in semantic relation extraction between named entities. This paper incorporates two kinds of lexical semantic similarity …		 Dandan Liu; Zhiwei Zhao; Yanan Hu; Longhua Qian; 2012-01-01
139 Distant Supervision Relation Extraction With Intra-Bag And Inter-Bag Attentions
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Highlight: This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision.		 Zhi-Xiu Ye; Zhen-Hua Ling,; naacl 2019-06-02
140 ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models Via Contrastive Learning
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Highlight: To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text.		 YUJIA QIN et. al. acl 2021-07-26
141 Distant Supervision Relation Extraction With Intra-Bag And Inter-Bag Attentions
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Highlight: This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision.		 Zhi-Xiu Ye; Zhen-Hua Ling; arxiv-cs.CL 2019-03-29
142 TempEval-3: Evaluating Events, Time Expressions, And Temporal Relations
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Highlight: We describe the TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise.		 NAUSHAD UZZAMAN et. al. arxiv-cs.CL 2012-06-22
143 Hierarchical Relation Extraction With Coarse-to-Fine Grained Attention
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Highlight: In this paper, we aim to incorporate the hierarchical information of relations for distantly supervised relation extraction and propose a novel hierarchical attention scheme.		 Xu Han; Pengfei Yu; Zhiyuan Liu; Maosong Sun; Peng Li; emnlp 2018-11-02
144 A Partition Filter Network for Joint Entity and Relation Extraction
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Highlight: We propose a partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter.		 Zhiheng Yan; Chong Zhang; Jinlan Fu; Qi Zhang; Zhongyu Wei; emnlp 2021-11-05
145 Graph Neural Networks With Generated Parameters For Relation Extraction
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Highlight: In this paper, we propose a novel graph neural network with generated parameters (GP-GNNs).		 HAO ZHU et. al. acl 2019-07-28
146 Graph Neural Networks With Generated Parameters For Relation Extraction
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Highlight: In this paper, we propose to generate the parameters of graph neural networks (GP-GNNs) according to natural language sentences, which enables GNNs to process relational reasoning on unstructured text inputs.		 HAO ZHU et. al. arxiv-cs.CL 2019-02-02
147 OneRel: Joint Entity and Relation Extraction with One Module in One Step
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Highlight: Therefore, previous joint methods suffer from the problems of cascading errors and redundant information. To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem.		 Yu-Ming Shang; Heyan Huang; Xianling Mao; aaai 2022-02-07
148 Unsupervised Relation Extraction By Mining Wikipedia Texts Using Information From The Web
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 Yulan Yan; Naoaki Okazaki; Yutaka Matsuo; Zhenglu Yang; Mitsuru Ishizuka; acl 2009-12-30
149 Joint Event And Temporal Relation Extraction With Shared Representations And Structured Prediction
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Highlight: We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction.		 Rujun Han; Qiang Ning; Nanyun Peng; arxiv-cs.CL 2019-09-02
150 Revisiting Relation Extraction in The Era of Large Language Models
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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
151 Revisiting Relation Extraction in The Era of Large Language Models
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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
152 Relation Extraction : A Survey
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Highlight: In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques.		 Sachin Pawar; Girish K. Palshikar; Pushpak Bhattacharyya; arxiv-cs.CL 2017-12-14
153 Neural Relation Extraction For Knowledge Base Enrichment
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Highlight: This way, NED errors may cause extraction errors that affect the overall precision and recall.To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model.		 Bayu Distiawan Trisedya; Gerhard Weikum; Jianzhong Qi; Rui Zhang,; acl 2019-07-28
154 Relation Extraction Using Label Propagation Based Semi-Supervised Learning
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 Jinxiu Chen; Donghong Ji; Chew Lim Tan; Zhengyu Niu; acl 2006-12-30
155 Improving Distantly Supervised Relation Extraction Using Word And Entity Based Attention
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Highlight: Our contributions in this paper are threefold.		 Sharmistha Jat; Siddhesh Khandelwal; Partha Talukdar; arxiv-cs.CL 2018-04-18
156 Fine-tuning Pre-Trained Transformer Language Models To Distantly Supervised Relation Extraction
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Highlight: To address this gap, we utilize a pre-trained language model, the OpenAI Generative Pre-trained Transformer (GPT) (Radford et al., 2018).		 Christoph Alt; Marc Hübner; Leonhard Hennig,; acl 2019-07-28
157 Fine-tuning Pre-Trained Transformer Language Models To Distantly Supervised Relation Extraction
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Highlight: To address this gap, we utilize a pre-trained language model, the OpenAI Generative Pre-trained Transformer (GPT) [Radford et al., 2018].		 Christoph Alt; Marc Hübner; Leonhard Hennig; arxiv-cs.CL 2019-06-19
158 FedED: Federated Learning Via Ensemble Distillation For Medical Relation Extraction
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Highlight: In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged.		 DIANBO SUI et. al. emnlp 2020-11-12
159 Exploring Syntactic Features For Relation Extraction Using A Convolution Tree Kernel
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 Min Zhang; Jie Zhang; Jian Su; naacl 2006-12-30
160 Fine-tune Bert For DocRED With Two-step Process
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Highlight: We argue that such simple baselines are not strong enough to model to complex interaction between entities.		 Hong Wang; Christfried Focke; Rob Sylvester; Nilesh Mishra; William Wang; arxiv-cs.CL 2019-09-26
161 Implicit Discourse Relation Classification Via Multi-Task Neural Networks
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Highlight: To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task.		 Yang Liu; Sujian Li; Xiaodong Zhang; Zhifang Sui; aaai 2016-02-12
162 Joint Inference of Entities, Relations, and Coreference
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Abstract: Although joint inference is an effective approach to avoid cascading of errors when inferring multiple natural language tasks, its application to information extraction has been …		 Sameer Singh; Sebastian Riedel; Brian Martin; Jiaping Zheng; Andrew McCallum; 2013-01-01
163 Global-to-Local Neural Networks For Document-Level Relation Extraction
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Highlight: In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations.		 Difeng Wang; Wei Hu; Ermei Cao; Weijian Sun; emnlp 2020-11-12
164 Semantic Relation Classification Via Bidirectional LSTM Networks With Entity-aware Attention Using Latent Entity Typing
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Highlight: To address these issues, we propose a novel end-to-end recurrent neural model which incorporates an entity-aware attention mechanism with a latent entity typing (LET) method.		 Joohong Lee; Sangwoo Seo; Yong Suk Choi; arxiv-cs.CL 2019-01-23
165 Relational Duality: Unsupervised Extraction Of Semantic Relations Between Entities On The Web
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Highlight: We use this dual representation of semantic relations to propose a novel sequential co-clustering algorithm that can extract numerous relations efficiently from unlabeled data.		 Danushka Tarupathi Bollegala; Yutaka Matsuo; Mitsuru Ishizuka; www 2010-05-13
166 A Relation-Specific Attention Network For Joint Entity And Relation Extraction
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Highlight: In this paper, we propose a relation-specific attention network (RSAN) to handle the issue.		 YUE YUAN et. al. ijcai 2020-07-21
167 Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction
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Highlight: In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation.		 Oscar Sainz; Oier Lopez de Lacalle; Gorka Labaka; Ander Barrena; Eneko Agirre; emnlp 2021-11-05
168 A Survey Of Deep Learning Methods For Relation Extraction
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Highlight: In this review, we compare the contributions and pitfalls of the various DL models that have been used for the task, to help guide the path ahead.		 Shantanu Kumar; arxiv-cs.CL 2017-05-10
169 Relation Extraction From Wikipedia Using Subtree Mining
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Highlight: In this study, we address the problem of extracting relations among entities from Wikipedia’s English articles, which in turn can serve for intelligent systems to satisfy users’ information needs.		 Dat P.T. Nguyen; Yutaka Matsuo; Mitsuru Ishizuka; aaai 2007-12-30
170 BioRED: A Rich Biomedical Relation Extraction Dataset
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Highlight: We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including BERT-based models, on the NER and RE tasks.		 Ling Luo; Po-Ting Lai; Chih-Hsuan Wei; Cecilia N Arighi; Zhiyong Lu; arxiv-cs.CL 2022-04-08
171 Joint Constrained Learning for Event-Event Relation Extraction
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Highlight: Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations.		 Haoyu Wang; Muhao Chen; Hongming Zhang; Dan Roth; arxiv-cs.CL 2020-10-13
172 Synchronous Double-channel Recurrent Network For Aspect-Opinion Pair Extraction
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Highlight: In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs. To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks.		 Shaowei Chen; Jie Liu; Yu Wang; Wenzheng Zhang; Ziming Chi; acl 2020-06-20
173 Learning The Extraction Order Of Multiple Relational Facts In A Sentence With Reinforcement Learning
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Highlight: In this paper we argue that the extraction order is important in this task.		 XIANGRONG ZENG et. al. emnlp 2019-11-02
174 UniRE: A Unified Label Space for Entity Relation Extraction
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Highlight: In this work, we propose to eliminate the different treatment on the two sub-tasks’ label spaces.		 YIJUN WANG et. al. acl 2021-07-26
175 Representation Iterative Fusion Based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction
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 Kang Zhao; Hua Xu; Yue Cheng; Xiaoteng Li; Kai Gao; Knowl. Based Syst. 2021-01-01
176 Effective Deep Memory Networks For Distant Supervised Relation Extraction
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Highlight: Typically it can be formalized as a multi-instance multi-label problem.In this paper, we introduce a novel neural approach for distant supervised (RE) with specific focus on attention mechanisms.Unlike the feature-based logistic regression model and compositional neural models such as CNN, our approach includes two major attention-based memory components, which is capable of explicitly capturing the importance of each context word for modeling the representation of the entity pair, as well as the intrinsic dependencies between relations.Such importance degree and dependency relationship are calculated with multiple computational layers, each of which is a neural attention model over an external memory.		 Xiaocheng Feng; Jiang Guo; Bing Qin; Ting Liu; Yongjie Liu; ijcai 2017-08-19
177 Spatial-Aware Graph Relation Network for Large-Scale Object Detection
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Highlight: In this work, we introduce a Spatial-aware Graph Relation Network (SGRN) to adaptive discover and incorporate key semantic and spatial relationships for reasoning over each object.		 Hang Xu; Chenhan Jiang; Xiaodan Liang; Zhenguo Li; cvpr 2019-06-14
178 Filling Knowledge Base Gaps For Distant Supervision Of Relation Extraction
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 Wei Xu; Raphael Hoffmann; Le Zhao; Ralph Grishman; acl 2013-12-30
179 Pattern-based Approaches to Semantic Relation Extraction: A State-of-the-art
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 Alain Auger; Caroline Barrière; 2008-01-01
180 Simple Customization Of Recursive Neural Networks For Semantic Relation Classification
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 Kazuma Hashimoto; Makoto Miwa; Yoshimasa Tsuruoka; Takashi Chikayama; emnlp 2013-12-30
181 A Review of Relation Extraction
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 Yu Yang; Huang Xun; You Hongliang; Data Analysis and Knowledge Discovery 2013-01-01
182 Weakly-supervised Relation Classification For Information Extraction
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Highlight: A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus.		 Zhu Zhang; cikm 2004-11-02
183 Deep Residual Learning For Weakly-Supervised Relation Extraction
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Highlight: In this paper, we design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction.		 Yi Yao Huang; William Yang Wang; emnlp 2017-11-07
184 Few-shot Relation Extraction Via Bayesian Meta-learning On Relation Graphs
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Highlight: We propose a novel Bayesian meta-learning approach to effectively learn the posterior distribution of the prototype vectors of relations, where the initial prior of the prototype vectors is parameterized with a graph neural network on the global relation graph.		 Meng Qu; Tianyu Gao; Louis-Pascal A. C. Xhonneux; Jian Tang; arxiv-cs.LG 2020-07-05
185 Classifying Temporal Relations By Bidirectional LSTM Over Dependency Paths
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Highlight: In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP).		 Fei Cheng; Yusuke Miyao; acl 2017-07-30
186 Combining Neural Networks And Log-linear Models To Improve Relation Extraction
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Highlight: This paper proposes to combine the traditional feature-based method, the convolutional and recurrent neural networks to simultaneously benefit from their advantages.		 Thien Huu Nguyen; Ralph Grishman; arxiv-cs.CL 2015-11-18
187 End-to-End Relation Extraction Using Distant Supervision From External Semantic Repositories
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 Truc-Vien T. Nguyen; Alessandro Moschitti; acl 2011-12-30
188 Large Scaled Relation Extraction With Reinforcement Learning
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Highlight: To solve this problem, we propose a novel model with reinforcement learning.		 Xiangrong Zeng; Shizhu He; Kang Liu; Jun Zhao; aaai 2018-02-02
189 SelfMatch: Robust Semisupervised Time‐series Classification with Self‐distillation
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Abstract: Over the years, a number of semisupervised deep‐learning algorithms have been proposed for time‐series classification (TSC). In semisupervised deep learning, from the point of …		 HUANLAI XING et. al. International Journal of Intelligent Systems 2022-11-01
190 Document-Level Relation Extraction with Reconstruction
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Highlight: To alleviate this issue, we propose a novel encoder-classifier-reconstructor model for DocRE.		 Wang Xu; Kehai Chen; Tiejun Zhao; aaai 2021-02-09
191 Effectiveness And Efficiency Of Open Relation Extraction
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 Filipe Mesquita; Jordan Schmidek; Denilson Barbosa; emnlp 2013-12-30
192 Neural Relation Extraction With Multi-lingual Attention
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Highlight: To address this issue, we introduce a multi-lingual neural relation extraction framework, which employs mono-lingual attention to utilize the information within mono-lingual texts and further proposes cross-lingual attention to consider the information consistency and complementarity among cross-lingual texts.		 Yankai Lin; Zhiyuan Liu; Maosong Sun; acl 2017-07-30
193 Extracting Multiple-Relations In One-Pass With Pre-Trained Transformers
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Highlight: In this work, we focus on the task of multiple relation extraction by encoding the paragraph only once (one-pass).		 HAOYU WANG et. al. arxiv-cs.CL 2019-02-03
194 A Deep Sequential Model For Discourse Parsing On Multi-Party Dialogues
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Highlight: This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues.		 Zhouxing Shi; Minlie Huang; aaai 2019-01-27
195 A Walk-based Model On Entity Graphs For Relation Extraction
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Highlight: We present a novel graph-based neural network model for relation extraction.		 Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou; arxiv-cs.CL 2019-02-19
196 Cascaded Human-Object Interaction Recognition
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Highlight: Considering the intrinsic complexity of the task, we introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding.		 Tianfei Zhou; Wenguan Wang; Siyuan Qi; Haibin Ling; Jianbing Shen; cvpr 2020-06-08
197 RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
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Highlight: We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods.		 Yew Ken Chia; Lidong Bing; Soujanya Poria; Luo Si; arxiv-cs.CL 2022-03-17
198 Packed Levitated Marker for Entity and Relation Extraction
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Highlight: In this work, we propose a novel span representation approach, named Packed Levitated Markers (PL-Marker), to consider the interrelation between the spans (pairs) by strategically packing the markers in the encoder.		 Deming Ye; Yankai Lin; Peng Li; Maosong Sun; acl 2022-05-17
199 Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks
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Highlight: In this paper, we propose a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN).		 Yuanhe Tian; Guimin Chen; Yan Song; Xiang Wan; acl 2021-07-26
200 Approaches to Text Mining for Clinical Medical Records
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Abstract: Clinical medical records contain a wealth of information, largely in free-text form. Means to extract structured information from free-text records is an important research …		 Xiaohua Zhou; Hyoil Han; Isaac Chankai; Ann Prestrud; Ari D. Brooks; 2006-01-01
201 UHop: An Unrestricted-Hop Relation Extraction Framework For Knowledge-Based Question Answering
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Highlight: In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a transition-based search framework to replace the relation-chain-based search one.		 Zi-Yuan Chen; Chih-Hung Chang; Yi-Pei Chen; Jijnasa Nayak; Lun-Wei Ku,; naacl 2019-06-02
202 Multilingual Relation Extraction Using Compositional Universal Schema
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 Patrick Verga; David Belanger; Emma Strubell; Benjamin Roth; Andrew McCallum; naacl 2016-12-30
203 UHop: An Unrestricted-Hop Relation Extraction Framework For Knowledge-Based Question Answering
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Highlight: In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a transition-based search framework to replace the relation-chain-based search one.		 Zi-Yuan Chen; Chih-Hung Chang; Yi-Pei Chen; Jijnasa Nayak; Lun-Wei Ku; arxiv-cs.CL 2019-04-02
204 A Structured Learning Approach To Temporal Relation Extraction
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Highlight: This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge.		 Qiang Ning; Zhili Feng; Dan Roth; arxiv-cs.CL 2019-06-12
205 Relation Extraction From Clinical Texts Using Domain Invariant Convolutional Neural Network
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Highlight: In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject of many research efforts and shared challenges.		 Sunil Kumar Sahu; Ashish Anand; Krishnadev Oruganty; Mahanandeeshwar Gattu; arxiv-cs.CL 2016-06-30
206 Attention-Based Capsule Networks With Dynamic Routing For Relation Extraction
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Highlight: In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms.		 NINGYU ZHANG et. al. arxiv-cs.IR 2018-12-29
207 Attention-Based Capsule Networks With Dynamic Routing For Relation Extraction
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Highlight: In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms.		 NINGYU ZHANG et. al. emnlp 2018-11-02
208 Neural Relation Extraction Within And Across Sentence Boundaries
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Highlight: In this paper, we propose a novel architecture for this task: inter-sentential dependency-based neural networks (iDepNN).		 Pankaj Gupta; Subburam Rajaram; Hinrich Schütze; Bernt Andrassy; Thomas Runkler; arxiv-cs.CL 2018-10-11
209 Neural Relation Extraction Within And Across Sentence Boundaries
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Highlight: In this paper, we propose a novel architecture for this task: inter-sentential dependency-based neural networks (iDepNN).		 Pankaj Gupta; Subburam Rajaram; Hinrich Schütze; Thomas Runkler; aaai 2019-01-27
210 Adversarial Connective-exploiting Networks For Implicit Discourse Relation Classification
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Highlight: We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification.		 Lianhui Qin; Zhisong Zhang; Hai Zhao; Zhiting Hu; Eric P. Xing; arxiv-cs.CL 2017-04-01
211 Adversarial Connective-exploiting Networks For Implicit Discourse Relation Classification
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Highlight: We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification.		 Lianhui Qin; Zhisong Zhang; Hai Zhao; Zhiting Hu; Eric Xing; acl 2017-07-30
212 Multimodal Relation Extraction with Efficient Graph Alignment
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Abstract: Relation extraction (RE) is a fundamental process in constructing knowledge graphs. However, previous methods on relation extraction suffer sharp performance decline in short and …		 CHANGMENG ZHENG et. al. Proceedings of the 29th ACM International Conference on … 2021-01-01
213 A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
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Highlight: In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly.		 Fei Li; ZhiChao Lin; Meishan Zhang; Donghong Ji; acl 2021-07-26
214 ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning
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Highlight: In this paper, we formulate the zero-shot relation extraction problem by incorporating the text description of seen and unseen relations.		 Chih-Yao Chen; Cheng-Te Li; naacl 2021-05-23
215 Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection
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Highlight: Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA).		 XINCHENG JU et. al. emnlp 2021-11-05
216 Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation
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Highlight: In this paper, we propose a semi-supervised framework for DocRE with three novel components.		 Qingyu Tan; Ruidan He; Lidong Bing; Hwee Tou Ng; arxiv-cs.CL 2022-03-21
217 N-ary Relation Extraction Using Graph State LSTM
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Highlight: We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing.		 Linfeng Song; Yue Zhang; Zhiguo Wang; Daniel Gildea; arxiv-cs.CL 2018-08-27
218 Discovering Relations Between Named Entities from A Large Raw Corpus Using Tree Similarity-Based Clustering
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Abstract: We propose a tree-similarity-based unsupervised learning method to extract relations between Named Entities from a large raw corpus. Our method regards relation extraction as a …		 Min Zhang; Jian Su; Danmei Wang; Guodong Zhou; Chew Lim Tan; 2005-01-01
219 Joint Type Inference On Entities And Relations Via Graph Convolutional Networks
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Highlight: To tackle the joint type inference task, we propose a novel graph convolutional network (GCN) running on an entity-relation bipartite graph.		 CHANGZHI SUN et. al. acl 2019-07-28
220 Improving Relation Extraction By Pre-trained Language Representations
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Highlight: To address these limitations, we introduce TRE, a Transformer for Relation Extraction, extending the OpenAI Generative Pre-trained Transformer [Radford et al., 2018].		 Christoph Alt; Marc Hübner; Leonhard Hennig; arxiv-cs.CL 2019-06-07
221 Relation Extraction Using Distant Supervision
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Abstract: Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows us to …		 Alisa Smirnova; Philippe Cudré-Mauroux; ACM Computing Surveys (CSUR) 2019-01-01
222 Improving Biomedical Pretrained Language Models with Knowledge
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Highlight: To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases.		 Zheng Yuan; Yijia Liu; Chuanqi Tan; Songfang Huang; Fei Huang; arxiv-cs.CL 2021-04-20
223 On The Importance Of Word And Sentence Representation Learning In Implicit Discourse Relation Classification
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Highlight: We argue that a powerful contextualized representation module, a bilateral multi-perspective matching module, and a global information fusion module are all important to implicit discourse analysis. We propose a novel model to combine these modules together.		 Xin Liu; Jiefu Ou; Yangqiu Song; Xin Jiang; ijcai 2020-07-21
224 Named Entity Recognition and Relation Extraction
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Abstract: With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense …		 Zara Nasar; S. Waqar Jaffry; Muhammad Kamran Malik; ACM Computing Surveys (CSUR) 2021-01-01
225 Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from The Web
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Abstract: We present a large-scale relation extraction (RE) system which learns grammar-based RE rules from the Web by utilizing large numbers of relation instances as seed. Our goal is to …		 Sebastian Krause; Hong Li; Hans Uszkoreit; Feiyu Xu; 2012-01-01
226 An Empirical Convolutional Neural Network Approach for Semantic Relation Classification
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 Pengda Qin; Weiran Xu; Jun Guo; Neurocomputing 2016-01-01
227 Fine-tuning BERT For Joint Entity And Relation Extraction In Chinese Medical Text
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Highlight: In this paper, we present a focused attention model for the joint entity and relation extraction task.		 KUI XUE et. al. arxiv-cs.CL 2019-08-21
228 Multi-Task Transfer Learning For Weakly-Supervised Relation Extraction
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 Jing Jiang; acl 2009-12-30
229 Incorporating Relation Paths In Neural Relation Extraction
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Highlight: To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains.		 Wenyuan Zeng; Yankai Lin; Zhiyuan Liu; Maosong Sun; emnlp 2017-11-07
230 Neural Architecture For Temporal Relation Extraction: A Bi-LSTM Approach For Detecting Narrative Containers
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Highlight: We present a neural architecture for containment relation identification between medical events and/or temporal expressions.		 Julien Tourille; Olivier Ferret; Aurélie Névéol; Xavier Tannier; acl 2017-07-30
231 Learning With Noise: Enhance Distantly Supervised Relation Extraction With Dynamic Transition Matrix
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Highlight: In this paper, we take a deep look at the application of distant supervision in relation extraction.		 BINGFENG LUO et. al. acl 2017-07-30
232 Tree Kernel-based Semantic Relation Extraction with Rich Syntactic and Semantic Information
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 Guodong Zhou; Longhua Qian; Jianxi Fan; Inf. Sci. 2010-01-01
233 Multi-way Relation Classification: Application To Protein-Protein Interactions
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 Barbara Rosario; Marti Hearst; emnlp 2005-12-30
234 Employing Word Representations And Regularization For Domain Adaptation Of Relation Extraction
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 Thien Huu Nguyen; Ralph Grishman; acl 2014-12-30
235 Multilingual Open Relation Extraction Using Cross-lingual Projection
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Highlight: We present a cross-lingual annotation projection method for language independent relation extraction.		 Manaal Faruqui; Shankar Kumar; arxiv-cs.CL 2015-03-22
236 GDPNet: Refining Latent Multi-View Graph for Relation Extraction
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Highlight: In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens.		 Fuzhao Xue; Aixin Sun; Hao Zhang; Eng Siong Chng; aaai 2021-02-09
237 Automatic Extraction of Hierarchical Relations from Text
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Abstract: Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an …		 Ting Wang; Yaoyong Li; Kalina Bontcheva; Hamish Cunningham; Ji Wang; 2006-01-01
238 Relation Extraction and Scoring in DeepQA
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Abstract: Detecting semantic relations in text is an active problem area in natural-language processing and information retrieval. For question answering, there are many advantages of …		 Chang Wang; Aditya Kalyanpur; James Fan; Branimir Boguraev; David Gondek; IBM J. Res. Dev. 2012-01-01
239 Video Relation Detection with Spatio-Temporal Graph
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Abstract: What we perceive from visual content are not only collections of objects but the interactions between them. Visual relations, denoted by the triplet , could convey a wealth of …		 XUFENG QIAN et. al. Proceedings of the 27th ACM International Conference on … 2019-01-01
240 Effective Crowd Annotation For Relation Extraction
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 ANGLI LIU et. al. naacl 2016-12-30
241 Implicit Discourse Relation Detection Via A Deep Architecture With Gated Relevance Network
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 Jifan Chen; Qi Zhang; Pengfei Liu; Xipeng Qiu; Xuanjing Huang; acl 2016-12-30
242 Extracting Temporal And Causal Relations Between Events
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Highlight: In this thesis we present a framework for an integrated temporal and causal relation extraction system.		 Paramita Mirza; arxiv-cs.CL 2016-04-27
243 Next Sentence Prediction Helps Implicit Discourse Relation Classification Within And Across Domains
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Highlight: We here show that this shortcoming can be effectively addressed by using the bidirectional encoder representation from transformers (BERT) proposed by Devlin et al. (2019), which were trained on a next-sentence prediction task, and thus encode a representation of likely next sentences.		 Wei Shi; Vera Demberg; emnlp 2019-11-02
244 Pattern Learning For Relation Extraction With A Hierarchical Topic Model
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 Enrique Alfonseca; Katja Filippova; Jean-Yves Delort; Guillermo Garrido; acl 2012-12-30
245 Measuring The Similarity Between Implicit Semantic Relations From The Web
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Highlight: We propose a relational similarity measure, using a Web search engine, to compute the similarity between semantic relations implied by two pairs of words.		 Danushka T. Bollegala; Yutaka Matsuo; Mitsuru Ishizuka; www 2009-05-13
246 Heterogeneous Supervision For Relation Extraction: A Representation Learning Approach
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Highlight: To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics.		 LIYUAN LIU et. al. emnlp 2017-11-07
247 Natural Language Processing – IJCNLP 2005, Second International Joint Conference, Jeju Island, Korea, October 11-13, 2005, Proceedings
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Abstract: Information Retrieval.- A New Method for Sentiment Classification in Text Retrieval.- Topic Tracking Based on Linguistic Features.- The Use of Monolingual Context Vectors for …		 Kam-Fai Wong; Jian Su; Oi Yee Kwong; 2005-01-01
248 RECON: Relation Extraction Using Knowledge Graph Context in A Graph Neural Network
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Highlight: In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).		 ANSON BASTOS et. al. www 2021-06-25
249 RECON: Relation Extraction Using Knowledge Graph Context in A Graph Neural Network
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Highlight: In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).		 ANSON BASTOS et. al. arxiv-cs.CL 2020-09-18
250 Semantic Inference At The Lexical-Syntactic Level
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Highlight: We propose a generic semantic inference framework that operates directly on syntactic trees.		 Roy Bar-Haim; Ido Dagan,Iddo Greental,Eyal Shnarch; aaai 2007-12-30
251 Exploring Syntactic Structured Features Over Parse Trees for Relation Extraction Using Kernel Methods
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 Min Zhang; Guodong Zhou; AiTi Aw; Inf. Process. Manag. 2008-01-01
252 Span-Level Model For Relation Extraction
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Highlight: To address these concerns, we present a model which directly models all possible spans and performs joint entity mention detection and relation extraction.		 Kalpit Dixit; Yaser Al-Onaizan,; acl 2019-07-28
253 Automatic Extraction Of Causal Relations From Natural Language Texts: A Comprehensive Survey
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Highlight: In this paper, we provide a comprehensive survey of causal relation extraction techniques from both paradigms, and analyse their relative strengths and weaknesses, with recommendations for future work.		 Nabiha Asghar; arxiv-cs.AI 2016-05-25
254 Medical Question Answering: Translating Medical Questions Into Sparql Queries
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Abstract: Designing question answering systems requires efficient and deep analysis of natural language questions. A key process for this task is to translate the semantic relations …		 Asma Ben Abacha; Pierre Zweigenbaum; 2012-01-01
255 Boundaries and Edges Rethinking: An End-to-end Neural Model for Overlapping Entity Relation Extraction
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 Hao Fei; Yafeng Ren; Donghong Ji; Inf. Process. Manag. 2020-01-01
256 GPT-RE: In-context Learning for Relation Extraction Using Large Language Models
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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
257 Label-Free Distant Supervision For Relation Extraction Via Knowledge Graph Embedding
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Highlight: This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.		 GUANYING WANG et. al. emnlp 2018-11-02
258 Neural Snowball for Few-Shot Relation Learning
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Highlight: To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations.		 TIANYU GAO et. al. aaai 2020-02-07
259 SelfORE: Self-supervised Relational Feature Learning For Open Relation Extraction
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Highlight: In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification.		 Xuming Hu; Lijie Wen; Yusong Xu; Chenwei Zhang; Philip Yu; emnlp 2020-11-12
260 SelfORE: Self-supervised Relational Feature Learning For Open Relation Extraction
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Highlight: In this work, we proposed a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification.		 Xuming Hu; Chenwei Zhang; Yusong Xu; Lijie Wen; Philip S. Yu; arxiv-cs.CL 2020-04-06
261 End-to-end Neural Relation Extraction Using Deep Biaffine Attention
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Highlight: We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features.		 Dat Quoc Nguyen; Karin Verspoor; arxiv-cs.CL 2018-12-28
262 Global Normalization Of Convolutional Neural Networks For Joint Entity And Relation Classification
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Highlight: We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction.		 Heike Adel; Hinrich Schütze; arxiv-cs.CL 2017-07-24
263 Cross-lingual Structure Transfer For Relation And Event Extraction
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Highlight: We investigate the suitability of cross-lingual structure transfer techniques for these tasks.		 ANANYA SUBBURATHINAM et. al. emnlp 2019-11-02
264 Chinese Relation Extraction With Multi-Grained Information And External Linguistic Knowledge
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Highlight: To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction to take advantage of multi-grained language information and external linguistic knowledge.		 Ziran Li; Ning Ding; Zhiyuan Liu; Haitao Zheng; Ying Shen,; acl 2019-07-28
265 Integer Relation Detection
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Abstract: Practical algorithms for integer relation detection have become a staple in the emerging discipline of experimental mathematics-using modern computer technology to explore …		 David H. Bailey; Computing in Science & Engineering 2000-01-01
266 A Joint Model for Entity and Relation Extraction Based on BERT
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Abstract: In recent years, as the knowledge graph has attained significant achievements in many specific fields, which has become one of the core driving forces for the development of the …		 BO QIAO et. al. Neural Computing and Applications 2021-01-01
267 Semi-supervised Relation Extraction Via Incremental Meta Self-Training
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Highlight: To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective.		 XUMING HU et. al. arxiv-cs.CL 2020-10-05
268 Clinical Relation Extraction with Deep Learning
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Abstract: Relations between medical concepts convey meaningful medical knowledge and patients’ health information. Relation extraction on Clinical texts is an important task of information …		 Xinbo Lv; Yi Guan; Jinfeng Yang; Jiawei Wu; International Journal of Hybrid Information Technology 2016-01-01
269 TransNet: Translation-Based Network Representation Learning For Social Relation Extraction
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Highlight: In this work, we present a novel Translation-based NRL model, TransNet, by regarding the interactions between vertices as a translation operation.		 Cunchao Tu; Zhengyan Zhang; Zhiyuan Liu; Maosong Sun; ijcai 2017-08-19
270 BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis
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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
271 Do Syntax Trees Help Pre-trained Transformers Extract Information?
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Highlight: In this work, we systematically study the utility of incorporating dependency trees into pre-trained transformers on three representative information extraction tasks: semantic role labeling (SRL), named entity recognition, and relation extraction.		 Devendra Singh Sachan; Yuhao Zhang; Peng Qi; William Hamilton; arxiv-cs.CL 2020-08-20
272 A Combination of RNN and CNN for Attention-based Relation Classification
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 Xiaobin Zhang; Fucai Chen; Ruiyang Huang; Procedia Computer Science 2018-01-01
273 Comparing Word Representations For Implicit Discourse Relation Classification
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 Chloé Braud; Pascal Denis; emnlp 2015-12-30
274 Exploiting Syntactic and Semantics Information for Chemical–disease Relation Extraction
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Abstract: Identifying chemical–disease relations (CDR) from biomedical literature could improve chemical safety and toxicity studies. This article proposes a novel syntactic and semantic …		 HUIWEI ZHOU et. al. Database: The Journal of Biological Databases and Curation 2016-04-14
275 CU-TMP: Temporal Relation Classification Using Syntactic and Semantic Features
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Abstract: We approached the temporal relation identification tasks of TempEval 2007 as pair-wise classification tasks. We introduced a variety of syntactically and semantically motivated …		 Steven Bethard; James H. Martin; 2007-01-01
276 Multi-Level Structured Self-Attentions For Distantly Supervised Relation Extraction
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Highlight: To alleviate this issue, we propose a novel multi-level structured (2-D matrix) self-attention mechanism for DS-RE in a multi-instance learning (MIL) framework using bidirectional recurrent neural networks (BiRNN).		 Jinhua Du; Jingguang Han; Andy Way; Dadong Wan; emnlp 2018-11-02
277 Mining Ontological Knowledge from Domain-specific Text Documents
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Abstract: Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called …		 Xing Jiang; Ah-Hwee Tan; Fifth IEEE International Conference on Data Mining (ICDM’05) 2005-01-01
278 Ensemble Semantics For Large-scale Unsupervised Relation Extraction
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 Bonan Min; Shuming Shi; Ralph Grishman; Chin-Yew Lin; emnlp 2012-12-30
279 SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
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Highlight: It is usually formulated as a classification problem that predicts relations for all entity pairs in the document.		 Shuang Zeng; Yuting Wu; Baobao Chang; arxiv-cs.CL 2021-06-03
280 Experiments With Three Approaches To Recognizing Lexical Entailment
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Highlight: In this paper, we experiment with two recent state-of-the-art representatives of the two general strategies.		 Peter D. Turney; Saif M. Mohammad; arxiv-cs.CL 2014-01-31
281 Relation Adversarial Network For Low Resource Knowledge Graph Completion
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Highlight: In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available.		 NINGYU ZHANG et. al. arxiv-cs.CL 2019-11-08
282 Relation Adversarial Network For Low Resource Knowledge Graph Completion
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Highlight: In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available.		 NINGYU ZHANG et. al. www 2020-05-13
283 Exploring Task Difficulty for Few-Shot Relation Extraction
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Highlight: In this paper, we introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information.		 Jiale Han; Bo Cheng; Wei Lu; emnlp 2021-11-05
284 Shallow Semantics For Relation Extraction
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 Sanda Harabagiu; Cosmin Adrian Bejan; and Paul Morarescu; ijcai 2005-12-30
285 CERES: Distantly Supervised Relation Extraction From The Semi-Structured Web
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Highlight: In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision.		 Colin Lockard; Xin Luna Dong; Arash Einolghozati; Prashant Shiralkar; arxiv-cs.AI 2018-04-12
286 Automatic Discovery of Personal Name Aliases from The Web
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Abstract: An individual is typically referred by numerous name aliases on the web. Accurate identification of aliases of a given person name is useful in various web related tasks such as …		 Danushka Bollegala; Yutaka Matsuo; Mitsuru Ishizuka; IEEE Transactions on Knowledge and Data Engineering 2011-01-01
287 Joint Entity and Relation Extraction Model Based on Rich Semantics
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 Zhiqiang Geng; Yanhui Zhang; Yongming Han; Neurocomputing 2021-01-01
288 Learning to Reason Deductively: Math Word Problem Solving As Complex Relation Extraction
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Highlight: In this work, we view the task as a complex relation extraction problem, proposing a novel approach that presents explainable deductive reasoning steps to iteratively construct target expressions, where each step involves a primitive operation over two quantities defining their relation.		 Zhanming Jie; Jierui Li; Wei Lu; arxiv-cs.CL 2022-03-19
289 An Empirical Study Of Pre-trained Transformers For Arabic Information Extraction
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Highlight: In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning.		 Wuwei Lan; Yang Chen; Wei Xu; Alan Ritter; emnlp 2020-11-12
290 Attention-Based Relation Extraction With Bidirectional Gated Recurrent Unit and Highway Network in The Analysis of Geological Data
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Abstract: Attention-based deep learning model as a human-centered smart technology has become the state-of-the-art method in addressing relation extraction, while implementing natural …		 Xiong Luo; Wenwen Zhou; Weiping Wang; Yueqin Zhu; Jing Deng; IEEE Access 2018-01-01
291 Zero-shot Temporal Relation Extraction with ChatGPT
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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
292 Graph Based Network with Contextualized Representations of Turns in Dialogue
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Highlight: In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues.		 Bongseok Lee; Yong Suk Choi; emnlp 2021-11-05
293 Relation Classification with Entity Type Restriction
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Highlight: In this paper, we propose a novel paradigm, RElation Classification with ENtity Type restriction (RECENT), which exploits entity types to restrict candidate relations.		 Shengfei Lyu; Huanhuan Chen; arxiv-cs.CL 2021-05-18
294 Noise Mitigation For Neural Entity Typing And Relation Extraction
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Highlight: In this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features.		 Yadollah Yaghoobzadeh; Heike Adel; Hinrich Schütze; arxiv-cs.CL 2016-12-22
295 Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts
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Abstract: To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence …		 Thorsten Barnickel; Jason Weston; Ronan Collobert; Hans-Werner Mewes; Volker Stümpflen; PLoS ONE 2009-01-01
296 A Stacking Gated Neural Architecture For Implicit Discourse Relation Classification
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 Lianhui Qin; Zhisong Zhang; Hai Zhao; emnlp 2016-12-30
297 Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
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Highlight: We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction.		 TONGTONG WU et. al. aaai 2021-02-09
298 Classifying Medical Relations In Clinical Text Via Convolutional Neural Networks
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Highlight: This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix.		 Bin He; Yi Guan; Rui Dai; arxiv-cs.CL 2018-05-17
299 Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
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Highlight: We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction.		 TONGTONG WU et. al. arxiv-cs.CL 2021-01-06
300 Mining Generalized Associations of Semantic Relations from Textual Web Content
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Abstract: Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. …		 Tao Jiang; Ah-Hwee Tan; Ke Wang; IEEE Transactions on Knowledge and Data Engineering 2007-01-01
301 Crowdsourcing Ground Truth for Medical Relation Extraction
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Abstract: Cognitive computing systems require human labeled data for evaluation and often for training. The standard practice used in gathering this data minimizes disagreement between …		 Anca Dumitrache; Lora Aroyo; Christopher A. Welty; ACM Transactions on Interactive Intelligent Systems (TiiS) 2018-01-01
302 DeepStruct: Pretraining of Language Models for Structure Prediction
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Highlight: We introduce a method for improving the structural understanding abilities of language models.		 CHENGUANG WANG et. al. arxiv-cs.CL 2022-05-20
303 Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks
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Highlight: This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE).		 Minh Van Nguyen; Viet Lai; Thien Huu Nguyen; naacl 2021-05-23
304 SEE: Syntax-Aware Entity Embedding For Neural Relation Extraction
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Highlight: In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction.		 ZHENGQIU HE et. al. aaai 2018-02-02
305 Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks
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Highlight: This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE).		 Minh Van Nguyen; Viet Dac Lai; Thien Huu Nguyen; arxiv-cs.CL 2021-03-16
306 Chemical–gene Relation Extraction Using Recursive Neural Network
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Abstract: In this article, we describe our system for the CHEMPROT task of the BioCreative VI challenge. Although considerable research on the named entity recognition of genes and drugs …		 Sangrak Lim; Jaewoo Kang; Database: The Journal of Biological Databases and Curation 2018-06-21
307 Distant Supervision For Relation Extraction With Matrix Completion
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 MIAO FAN et. al. acl 2014-12-30
308 Biological Relation Extraction and Query Answering from MEDLINE Abstracts Using Ontology-based Text Mining
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 Muhammad Abulaish; Lipika Dey; Data Knowl. Eng. 2007-01-01
309 Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
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Highlight: Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference).		 Tuan Lai; Heng Ji; ChengXiang Zhai; Quan Hung Tran; acl 2021-07-26
310 A Neural Network Framework for Relation Extraction: Learning Entity Semantic and Relation Pattern
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 SUNCONG ZHENG et. al. Knowl. Based Syst. 2016-01-01
311 Aligning Instruction Tasks Unlocks Large Language Models As Zero-Shot Relation Extractors
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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
312 Multi-Labeled Relation Extraction With Attentive Capsule Network
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Highlight: To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence.		 Xinsong Zhang; Pengshuai Li; Weijia Jia; Hai Zhao; aaai 2019-01-27
313 Cross-Relation Cross-Bag Attention For Distantly-Supervised Relation Extraction
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Highlight: In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor.		 YUJIN YUAN et. al. aaai 2019-01-27
314 Interventional Video Relation Detection
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Abstract: Video Visual Relation Detection (VidVRD) aims to semantically describe the dynamic interactions across visual concepts localized in a video in the form of subject, predicate, …		 Yicong Li; Xun Yang; Xindi Shang; Tat-Seng Chua; Proceedings of the 29th ACM International Conference on … 2021-01-01
315 Entity Concept-enhanced Few-shot Relation Extraction
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Highlight: To address this problem, in this paper, we proposed a novel entity CONCEPT-enhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance.		 Shan Yang; Yongfei Zhang; Guanglin Niu; Qinghua Zhao; Shiliang Pu; acl 2021-07-26
316 Chain Based RNN For Relation Classification
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 Javid Ebrahimi; Dejing Dou; naacl 2015-12-30
317 Definitional Verbal Patterns for Semantic Relation Extraction
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Abstract: In this paper we present a description of the role of definitional verbal patterns for the extraction of semantic relations. Several studies show that semantic relations can be …		 Gerardo Sierra; Rodrigo Alarcón; César Aguilar; Carme Bach; Terminology 2008-01-01
318 Self-supervised Relation Extraction from The Web
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Abstract: Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional Information …		 Benjamin Rozenfeld; Ronen Feldman; Knowledge and Information Systems 2007-01-01
319 Learning Dual Retrieval Module For Semi-supervised Relation Extraction
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Highlight: In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting the relation label for a given sentence-two tasks are complementary to each other and can be optimized jointly for mutual enhancement.		 Hongtao Lin; Jun Yan; Meng Qu; Xiang Ren; www 2019-05-13
320 Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion
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Highlight: In this paper, we propose an evidence-enhanced framework, Eider, that empowers DocRE by efficiently extracting evidence and effectively fusing the extracted evidence in inference.		 Yiqing Xie; Jiaming Shen; Sha Li; Yuning Mao; Jiawei Han; arxiv-cs.CL 2021-06-16
321 Deep Neural Network-based Relation Extraction: An Overview
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Abstract: Knowledge is a formal way of understanding the world, providing human-level cognition and intelligence for the next-generation artificial intelligence (AI). An effective way to …		 Hailin Wang; Ke Qin; Rufai Yusuf Zakari; Guisong Liu; Guoming Lu; Neural Computing and Applications 2022-01-01
322 Fine-Grained Temporal Relation Extraction
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Highlight: We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales.		 Siddharth Vashishtha; Benjamin Van Durme; Aaron Steven White,; acl 2019-07-28
323 Neural Relation Extraction Via Inner-Sentence Noise Reduction And Transfer Learning
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Highlight: To mitigate this problem, we propose a novel word-level distant supervised approach for relation extraction.		 Tianyi Liu; Xinsong Zhang; Wanhao Zhou; Weijia Jia; arxiv-cs.CL 2018-08-20
324 Fine-Grained Temporal Relation Extraction
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Highlight: We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales.		 Siddharth Vashishtha; Benjamin Van Durme; Aaron Steven White; arxiv-cs.CL 2019-02-04
325 Improving Implicit Discourse Relation Classification By Modeling Inter-dependencies Of Discourse Units In A Paragraph
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Highlight: With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph.		 Zeyu Dai; Ruihong Huang; naacl 2018-11-02
326 Extracting Relation Information from Text Documents By Exploring Various Types of Knowledge
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 Guodong Zhou; Min Zhang; Inf. Process. Manag. 2007-01-01
327 Building and Querying An Enterprise Knowledge Graph
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Abstract: Information providers are faced with a critical challenge to process, retrieve and present information to their users in order to satisfy their complex information needs, because …		 DEZHAO SONG et. al. IEEE Transactions on Services Computing 2019-01-01
328 Effective Slot Filling Based On Shallow Distant Supervision Methods
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Abstract: Spoken Language Systems at Saarland University (LSV) participated this year with 5 runs at the TAC KBP English slot filling track. Effective algorithms for all parts of the …		 Benjamin Roth; Tassilo Barth; Michael Wiegand; Mittul Singh; Dietrich Klakow; arxiv-cs.CL 2014-01-06
329 Biomedical Natural Language Processing
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Abstract: The book begins with a declaration that “the intended audience of the book is natural language processing specialists who want to move into the biomedical domain.” It is indeed a …		 Jin-Dong Kim; Computational Linguistics 2017-01-01
330 Discriminative Reasoning for Document-level Relation Extraction
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Highlight: In this paper, we propose a novel discriminative reasoning framework to explicitly model the paths of these reasoning skills between each entity pair in this document.		 Wang Xu; Kehai Chen; Tiejun Zhao; arxiv-cs.CL 2021-06-02
331 A Survey of Noise Reduction Methods for Distant Supervision
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Abstract: We survey recent approaches to noise reduction in distant supervision learning for relation extraction. We group them according to the principles they are based on: at-least-one …		 Benjamin Roth; Tassilo Barth; Michael Wiegand; Dietrich Klakow; 2013-01-01
332 An End-to-end Model for Entity-level Relation Extraction Using Multi-instance Learning
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Highlight: We present a joint model for entity-level relation extraction from documents.		 Markus Eberts; Adrian Ulges; arxiv-cs.CL 2021-02-11
333 Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
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Highlight: To alleviate selection bias due to the lack of feedback loops in existing LRE learning paradigms, we developed a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate the gradient descent direction on labeled data and bootstrap its optimization capability through trial and error.		 XUMING HU et. al. emnlp 2021-11-05
334 Integrating Co-occurrence Statistics with Information Extraction for Robust Retrieval of Protein Interactions from Medline
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Abstract: The task of mining relations from collections of documents is usually approached in two different ways. One type of systems do relation extraction from individual sentences, …		 Razvan C. Bunescu; Raymond J. Mooney; Arun K. Ramani; Edward M. Marcotte; 2006-01-01
335 Revisiting DocRED – Addressing The False Negative Problem in Relation Extraction
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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
336 Temporal Reasoning Graph For Activity Recognition
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Highlight: In this paper, we propose an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales.		 Jingran Zhang; Fumin Shen; Xing Xu; Heng Tao Shen; arxiv-cs.CV 2019-08-26
337 PORE: Positive-Only Relation Extraction from Wikipedia Text
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Abstract: Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free …		 Gang Wang; Yong Yu; Haiping Zhu; 2007-01-01
338 Using Corpus Statistics On Entities To Improve Semi-supervised Relation Extraction From The Web
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 Benjamin Rosenfeld; Ronen Feldman; acl 2007-12-30
339 Review of Relation Extraction Methods: What Is New Out There?
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Abstract: Relation extraction is a part of Information Extraction and an established task in Natural Language Processing. This paper presents an overview of the main directions of research …		 Natalia Konstantinova; 2014-01-01
340 Logician: A Unified End-to-End Neural Approach For Open-Domain Information Extraction
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Highlight: In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We publicly release a data set which contains more than forty thousand sentences and the corresponding facts in the SAOKE format labeled by crowd-sourcing.		 MINGMING SUN et. al. arxiv-cs.CL 2019-04-29
341 ReVersion: Diffusion-Based Relation Inversion from Images
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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
342 ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations
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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
343 Deep Structured Neural Network For Event Temporal Relation Extraction
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Highlight: We propose a novel deep structured learning framework for event temporal relation extraction.		 RUJUN HAN et. al. arxiv-cs.CL 2019-09-22
344 ARNOR: Attention Regularization Based Noise Reduction For Distant Supervision Relation Classification
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Highlight: In this paper, we propose ARNOR, a novel Attention Regularization based NOise Reduction framework for distant supervision relation classification.		 Wei Jia; Dai Dai; Xinyan Xiao; Hua Wu,; acl 2019-07-28
345 Relation Extraction
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 Vittorio Castelli; Imed Zitouni; 2014-01-01
346 Model-Agnostic Meta-Learning For Relation Classification With Limited Supervision
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Highlight: In this paper we frame the task of supervised relation classification as an instance of meta-learning.		 Abiola Obamuyide; Andreas Vlachos,; acl 2019-07-28
347 DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction
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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
348 Incorporating Medical Knowledge in BERT for Clinical Relation Extraction
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Highlight: To solve these issues, in this research, we conduct a comprehensive examination of different techniques to add medical knowledge into a pre-trained BERT model for clinical relation extraction.		 Arpita Roy; Shimei Pan; emnlp 2021-11-05
349 Open Relation Extraction: Relational Knowledge Transfer From Supervised Data To Unsupervised Data
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Highlight: To address this issue, we propose Relational Siamese Networks (RSNs) to learn similarity metrics of relations from labeled data of pre-defined relations, and then transfer the relational knowledge to identify novel relations in unlabeled data.		 RUIDONG WU et. al. emnlp 2019-11-02
350 DWIE: An Entity-centric Dataset for Multi-task Document-level Information Extraction
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Highlight: This paper presents DWIE, the ‘Deutsche Welle corpus for Information Extraction’, a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking.		 Klim Zaporojets; Johannes Deleu; Chris Develder; Thomas Demeester; arxiv-cs.CL 2020-09-26
351 A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
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Highlight: To this end, we present a Trigger-Sense Memory Flow Framework (TriMF) for joint entity and relation extraction.		 Yongliang Shen; Xinyin Ma; Yechun Tang; Weiming Lu; www 2021-06-25
352 MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
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Highlight: Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes.		 XIAOZHI WANG et. al. emnlp 2022-12-30
353 Re-Ranking Algorithms for Name Tagging
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Abstract: Integrating information from different stages of an NLP processing pipeline can yield significant error reduction. We demonstrate how re-ranking can improve name tagging in a …		 Heng Ji; Cynthia Rudin; Ralph Grishman; 2006-01-01
354 Relation Extraction and The Influence of Automatic Named-entity Recognition
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Abstract: We present an approach for extracting relations between named entities from natural language documents. The approach is based solely on shallow linguistic processing, such as …		 Claudio Giuliano; Alberto Lavelli; Lorenza Romano; ACM Trans. Speech Lang. Process. 2007-01-01
355 ZeroShotCeres: Zero-Shot Relation Extraction From Semi-Structured Webpages
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Highlight: In this work, we propose a solution for zero-shot open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals.		 Colin Lockard; Prashant Shiralkar; Xin Luna Dong; Hannaneh Hajishirzi; acl 2020-06-20
356 Enhance Prototypical Network with Text Descriptions for Few-shot Relation Classification
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Highlight: In this paper, we propose TD-Proto, which enhances prototypical network with relation and entity descriptions.		 KAIJIA YANG et. al. cikm 2020-10-19
357 Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction
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Highlight: To fully utilize memorized samples, in this paper, we employ relation prototype to extract useful information of each relation.		 LI CUI et. al. acl 2021-07-26
358 Exploring Semi-supervised Variational Autoencoders For Biomedical Relation Extraction
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Highlight: We present a novel semi-supervised approach based on variational autoencoder (VAE) for biomedical relation extraction.		 Yijia Zhang; Zhiyong Lu; arxiv-cs.CL 2019-01-18
359 Type-Aware Distantly Supervised Relation Extraction With Linked Arguments
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 Mitchell Koch; John Gilmer; Stephen Soderland; Daniel S. Weld; emnlp 2014-12-30
360 A Discourse-Level Named Entity Recognition And Relation Extraction Dataset For Chinese Literature Text
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Highlight: To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method. In this paper, we build a discourse-level dataset from hundreds of Chinese literature articles for improving this task.		 Jingjing Xu; Ji Wen; Xu Sun; Qi Su; arxiv-cs.CL 2017-11-19
361 Infusion Of Labeled Data Into Distant Supervision For Relation Extraction
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 Maria Pershina; Bonan Min; Wei Xu; Ralph Grishman; acl 2014-12-30
362 Prototypical Representation Learning for Relation Extraction
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Highlight: Instead of solely relying on the supervision from noisy labels, we propose to learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations.		 NING DING et. al. arxiv-cs.CL 2021-03-22
363 Task-Oriented Learning Of Word Embeddings For Semantic Relation Classification
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Highlight: We present a novel learning method for word embeddings designed for relation classification.		 Kazuma Hashimoto; Pontus Stenetorp; Makoto Miwa; Yoshimasa Tsuruoka; arxiv-cs.CL 2015-02-28
364 CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction
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Highlight: In this paper, we go beyond typical MIL framework and propose a novel contrastive instance learning (CIL) framework.		 TAO CHEN et. al. acl 2021-07-26
365 Extracting Entities And Relations With Joint Minimum Risk Training
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Highlight: Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT).		 CHANGZHI SUN et. al. emnlp 2018-11-02
366 MNRE: A Challenge Multimodal Dataset for Neural Relation Extraction with Visual Evidence in Social Media Posts
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Abstract: Extracting relations in social media posts is challenging when sentences lack of contexts. However, images related to these sentences can supplement such missing contexts and help …		 Changmeng Zheng; Zhiwei Wu; Junhao Feng; Ze Fu; Yi Cai; 2021 IEEE International Conference on Multimedia and Expo … 2021-01-01
367 NERO: A Neural Rule Grounding Framework For Label-Efficient Relation Extraction
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Highlight: In this paper, we present a neural approach to ground rules for RE, named Nero, which jointly learns a relation extraction module and a soft matching module.		 WENXUAN ZHOU et. al. www 2020-05-13
368 Attention As Relation: Learning Supervised Multi-head Self-Attention For Relation Extraction
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Highlight: In this paper, we propose an attention-based joint model, which mainly contains an entity extraction module and a relation detection module, to address the challenges.		 JIE LIU et. al. ijcai 2020-07-21
369 Knowledge Representation Learning: A Quantitative Review
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Highlight: In this article, we introduce the reader to the motivations for KRL, and overview existing approaches for KRL.		 Yankai Lin; Xu Han; Ruobing Xie; Zhiyuan Liu; Maosong Sun; arxiv-cs.CL 2018-12-28
370 FBK-IRST: Kernel Methods for Semantic Relation Extraction
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Abstract: We present an approach for semantic relation extraction between nominals that combines shallow and deep syntactic processing and semantic information using kernel methods. Two …		 Claudio Giuliano; Alberto Lavelli; Daniele Pighin; Lorenza Romano; 2007-01-01
371 Subtree Mining For Relation Extraction From Wikipedia
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 Dat P.T. Nguyen; Yutaka Matsuo; Mitsuru Ishizuka; naacl 2007-12-30
372 Fine-grained Named Entity Recognition And Relation Extraction For Question Answering
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Abstract: No abstract available. …		 Changki Lee; Yi-Gyu Hwang; Myung-Gil Jang; sigir 2007-07-29
373 Numerical Relation Extraction With Minimal Supervision
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Highlight: We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model.		 Aman Madaan; Ashish Mittal; . Mausam; Ganesh Ramakrishnan; Sunita Sarawagi; aaai 2016-02-12
374 Digital Image Watermarking Capacity and Detection Error Rate
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 Fan Zhang; Xinhong Zhang; Hongbin Zhang; Pattern Recognit. Lett. 2007-01-01
375 Summarization As Indirect Supervision for Relation Extraction
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Highlight: We present SuRE, which converts RE into a summarization formulation.		 Keming Lu; I-Hung Hsu; Wenxuan Zhou; Mingyu Derek Ma; Muhao Chen; arxiv-cs.CL 2022-05-19
376 Using A Knowledge Graph and Query Click Logs for Unsupervised Learning of Relation Detection
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Abstract: In this paper, we introduce a novel statistical language understanding paradigm inspired by the emerging semantic web: Instead of building models for the target application, we …		 Dilek Z. Hakkani-Tür; Larry P. Heck; Gökhan Tür; 2013 IEEE International Conference on Acoustics, Speech and … 2013-01-01
377 Weakly-supervised Relation Extraction By Pattern-enhanced Embedding Learning
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Highlight: In this paper, we study the integration of distributional and pattern-based methods in a weakly-supervised setting such that the two kinds of methods can provide complementary supervision for each other to build an effective, unified model.		 Meng Qu; Xiang Ren; Yu Zhang; Jiawei Han; www 2018-05-13
378 Anticipating Annotations And Emerging Trends In Biomedical Literature
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Highlight: We present two novel technologies that are related to the analysis of temporal dynamics of text archives and associated ontologies.		 FABIAN MÖRCHEN et. al. kdd 2008-12-30
379 Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
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Highlight: In this paper, we propose the CoRE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.		 YIWEI WANG et. al. naacl 2022-07-09
380 Combining Word Embeddings And Feature Embeddings For Fine-grained Relation Extraction
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 Mo Yu; Matthew R. Gormley; Mark Dredze; naacl 2015-12-30
381 Paths to Relation Extraction Through Semantic Structure
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Abstract: Syntactic and semantic structure directly reflect relations expressed by the text at hand and are thus very useful for the relation extraction (RE) task. Their symbolic nature …		 Jonathan Yellin; Omri Abend; 2021-01-01
382 Large-scale Directional Relationship Extraction and Resolution
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Abstract: BackgroundRelationships between entities such as genes, chemicals, metabolites, phenotypes and diseases in MEDLINE are often directional. That is, one may affect the other in a …		 Cory B. Giles; Jonathan D. Wren; BMC Bioinformatics 2008-01-01
383 Rule-Based Extraction of Spatial Relations in Natural Language Text
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Abstract: Intelligent geographical information systems (GISs) have been paid much attention in recent years, and the ultimate goal is to realize the natural language interaction between …		 Chunju Zhang; Xueying Zhang; Wenming Jiang; Qijun Shen; Shanqi Zhang; 2009 International Conference on Computational Intelligence … 2009-01-01
384 A Sequence-to-sequence Approach for Document-level Relation Extraction
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Highlight: In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components.		 John Giorgi; Gary D. Bader; Bo Wang; arxiv-cs.CL 2022-04-03
385 Fine-grained Sentiment Analysis With Faithful Attention
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Highlight: Surprisingly, we found that despite reasonable performance, the model’s attention was often systematically misaligned with the words that contribute to sentiment.		 Ruiqi Zhong; Steven Shao; Kathleen McKeown; arxiv-cs.CL 2019-08-19
386 Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction
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Highlight: In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods.		 QUZHE HUANG et. al. acl 2021-07-26
387 Experiments with Reasoning for Temporal Relations Between Events
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Abstract: Few attempts have been made to investigate the utility of temporal reasoning within machine learning frameworks for temporal relation classification between events in news …		 Marta Tatu; Munirathnam Srikanth; 2008-01-01
388 Unsupervised Open Relation Extraction
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Highlight: We explore methods to extract relations between named entities from free text in an unsupervised setting.		 Hady Elsahar; Elena Demidova; Simon Gottschalk; Christophe Gravier; Frederique Laforest; arxiv-cs.CL 2018-01-22
389 ZORE: A Syntax-based System For Chinese Open Relation Extraction
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 Likun Qiu; Yue Zhang; emnlp 2014-12-30
390 Mining Clinical Relationships from Patient Narratives
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Abstract: BackgroundThe Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order …		 Angus Roberts; Robert J. Gaizauskas; Mark Hepple; Yikun Guo; BMC Bioinformatics 2008-01-01
391 REFORM: Error-Aware Few-Shot Knowledge Graph Completion
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Highlight: To tackle the aforementioned issues, in this paper, we study a novel problem of error-aware few-shot KG completion and present a principled KG completion framework REFORM.		 Song Wang; Xiao Huang; Chen Chen; Liang Wu; Jundong Li; cikm 2021-12-30
392 Integrating Deep Learning with Logic Fusion for Information Extraction
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Highlight: To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner.		 Wenya Wang; Sinno Jialin Pan; aaai 2020-02-07
393 Relation Classification Via Recurrent Neural Network with Attention and Tensor Layers
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Abstract: Relation classification is a crucial component in many Natural Language Processing (NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network …		 Runyan Zhang; Fan-Rong Meng; Yong Zhou; Bing Liu; Big Data Min. Anal. 2018-01-01
394 Using Semantic Similarity to Reduce Wrong Labels in Distant Supervision for Relation Extraction
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 Chengsen Ru; Jintao Tang; Shasha Li; Songxian Xie; Ting Wang; Inf. Process. Manag. 2018-01-01
395 Turning User Generated Health-related Content Into Actionable Knowledge Through Text Analytics Services
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 PALOMA MARTÍNEZ et. al. Comput. Ind. 2016-01-01
396 BERT-GT: Cross-sentence N-ary Relation Extraction with BERT and Graph Transformer
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Highlight: In this work, we propose a novel architecture that combines Bidirectional Encoder Representations from Transformers with Graph Transformer (BERT-GT), through integrating a neighbor-attention mechanism into the BERT architecture.		 Po-Ting Lai; Zhiyong Lu; arxiv-cs.CL 2021-01-11
397 Beyond Word Attention: Using Segment Attention In Neural Relation Extraction
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Highlight: In this paper, we aim to incorporate such segment information into neural relation extractor.		 BOWEN YU et. al. ijcai 2019-08-09
398 Probabilistic Enrichment of Knowledge Graph Entities for Relation Detection in Conversational Understanding
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Abstract: Knowledge encoded in semantic graphs such as Freebase has been shown to benefit semantic parsing and interpretation of natural language user utterances. In this paper, we propose …		 Dilek Z. Hakkani-Tür; Asli Celikyilmaz; Larry Heck; Gökhan Tür; G. Zweig; Interspeech 2014-09-01
399 Text-to-Table: A New Way of Information Extraction
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Highlight: In this work, we formalize text-to-table as a sequence-to-sequence (seq2seq) problem.		 Xueqing Wu; Jiacheng Zhang; Hang Li; acl 2022-05-17
400 Relation Extraction with Convolutional Network Over Learnable Syntax-Transport Graph
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Highlight: In this work, we learn to transform the dependency tree into a weighted graph by considering the syntax dependencies of the connected nodes and persisting the structure of the original dependency tree.		 Kai Sun; Richong Zhang; Yongyi Mao; Samuel Mensah; Xudong Liu; aaai 2020-02-07
401 Chinese Open Relation Extraction and Knowledge Base Establishment
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Abstract: Named entity relation extraction is an important subject in the field of information extraction. Although many English extractors have achieved reasonable performance, an …		 Shengbin Jia; E Shijia; Maozhen Li; Yang Xiang; ACM Transactions on Asian and Low-Resource Language … 2018-01-01
402 Video Visual Relation Detection Via Multi-modal Feature Fusion
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Abstract: Video visual relation detection is a meaningful research problem, which aims to build a bridge between dynamic vision and language. In this paper, we propose a novel video visual …		 Xu Sun; Tongwei Ren; Yuan Zi; Gangshan Wu; Proceedings of the 27th ACM International Conference on … 2019-01-01
403 Predicting Accuracy Of Extracting Information From Unstructured Text Collections
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Highlight: We present a general language modeling method for quantifying the difficulty of information extraction tasks.		 Eugene Agichtein; Silviu Cucerzan; cikm 2005-11-02
404 Convolutional Neural Networks For Chemical-disease Relation Extraction Are Improved With Character-based Word Embeddings
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Highlight: We investigate the incorporation of character-based word representations into a standard CNN-based relation extraction model.		 Dat Quoc Nguyen; Karin Verspoor; arxiv-cs.CL 2018-05-27
405 Consistent Representation Learning for Continual Relation Extraction
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Highlight: However, these memory-based methods tend to overfit the memory samples and perform poorly on imbalanced datasets. To solve these challenges, a consistent representation learning method is proposed, which maintains the stability of the relation embedding by adopting contrastive learning and knowledge distillation when replaying memory.		 Kang Zhao; Hua Xu; Jiangong Yang; Kai Gao; arxiv-cs.CL 2022-03-05
406 Punctuating Speech for Information Extraction
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Abstract: This paper studies the effect of automatic sentence boundary detection and comma prediction on entity and relation extraction in speech. We show that punctuating the machine …		 BENOÎT FAVRE et. al. 2008 IEEE International Conference on Acoustics, Speech and … 2008-01-01
407 Comparing Convolutional Neural Networks To Traditional Models For Slot Filling
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Highlight: We propose a convolutional neural network which splits the input sentence into three parts according to the relation arguments and compare it to state-of-the-art and traditional approaches of relation classification.		 Heike Adel; Benjamin Roth; Hinrich Schütze; arxiv-cs.CL 2016-03-16
408 Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City
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Abstract: The explosive data growth in smart city is making domain big data a hot topic for knowledge extraction. Non-taxonomic relations refer to any relations between concept pairs except …		 JING QIU et. al. IEEE Access 2018-01-01
409 URES : An Unsupervised Web Relation Extraction System
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 Benjamin Rosenfeld; Ronen Feldman; acl 2006-12-30
410 Implicit Discourse Relation Classification: We Need To Talk About Evaluation
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Highlight: In this work, we highlight these inconsistencies and propose an improved evaluation protocol.		 Najoung Kim; Song Feng; Chulaka Gunasekara; Luis Lastras; acl 2020-06-20
411 Concept Relation Extraction Using Naïve Bayes Classifier for Ontology-based Question Answering Systems
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 G. Suresh kumar; G. Zayaraz; J. King Saud Univ. Comput. Inf. Sci. 2015-01-01
412 Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding
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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
413 Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
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Highlight: To help future research, we present a comprehensive review of the recently published research works in relation extraction.		 Tapas Nayak; Navonil Majumder; Pawan Goyal; Soujanya Poria; arxiv-cs.CL 2021-03-31
414 Information Extraction from Text Intensive and Visually Rich Banking Documents
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 Berke Oral; Erdem Emekligil; Secil Arslan; Gülsen Eryiǧit; Inf. Process. Manag. 2020-01-01
415 Joint Multimodal Entity-Relation Extraction Based on Edge-Enhanced Graph Alignment Network and Word-Pair Relation Tagging
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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
416 Relation Extraction With Relation Topics
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 Chang Wang; James Fan; Aditya Kalyanpur; David Gondek; emnlp 2011-12-30
417 Medical Relation Extraction With Manifold Models
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 Chang Wang; James Fan; acl 2014-12-30
418 Joint Representation Learning Of Text And Knowledge For Knowledge Graph Completion
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Highlight: In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space.		 Xu Han; Zhiyuan Liu; Maosong Sun; arxiv-cs.CL 2016-11-13
419 Named Entity and Relation Extraction with Multi-Modal Retrieval
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Highlight: In this paper, we propose a novel Multi-modal Retrieval based framework (MoRe).		 XINYU WANG et. al. arxiv-cs.CL 2022-12-03
420 TIMERS: Document-level Temporal Relation Extraction
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Highlight: We present TIMERS – a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language.		 PUNEET MATHUR et. al. acl 2021-07-26
421 Improving Distantly-Supervised Relation Extraction With Joint Label Embedding
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Highlight: In this paper, we propose a novel multi-layer attention-based model to improve relation extraction with joint label embedding.		 LINMEI HU et. al. emnlp 2019-11-02
422 SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
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Highlight: In contrast, we propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE.		 Yuxin Xiao; Zecheng Zhang; Yuning Mao; Carl Yang; Jiawei Han; naacl 2022-07-09
423 ExpBERT: Representation Engineering With Natural Language Explanations
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Highlight: In this paper, we allow model developers to specify these types of inductive biases as natural language explanations.		 Shikhar Murty; Pang Wei Koh; Percy Liang; acl 2020-06-20
424 Improving Name Tagging By Reference Resolution And Relation Detection
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 Heng Ji; Ralph Grishman; acl 2005-12-30
425 Frowning Frodo, Wincing Leia, And A Seriously Great Friendship: Learning To Classify Emotional Relationships Of Fictional Characters
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Highlight: In this paper, we combine these aspects into a unified framework to classify emotional relationships of fictional characters.		 Evgeny Kim; Roman Klinger; arxiv-cs.CL 2019-03-29
426 Drug Name Recognition: Approaches and Resources
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Abstract: Drug name recognition (DNR), which seeks to recognize drug mentions in unstructured medical texts and classify them into pre-defined categories, is a fundamental task of medical …		 Shengyu Liu; Buzhou Tang; Qingcai Chen; Xiaolong Wang; Inf. 2015-01-01
427 Towards Knowledge Acquisition from Information Extraction
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Abstract: In our research to use information extraction to help populate the semantic web, we have encountered significant obstacles to interoperability between the technologies. We believe …		 Christopher A. Welty; J. William Murdock; 2006-01-01
428 Frowning Frodo, Wincing Leia, And A Seriously Great Friendship: Learning To Classify Emotional Relationships Of Fictional Characters
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Highlight: In this paper, we combine these aspects into a unified framework to classify emotional relationships of fictional characters.		 Evgeny Kim; Roman Klinger,; naacl 2019-06-02
429 Automatic Relationship Extraction from Agricultural Text for Ontology Construction
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 Neha Kaushik; Niladri Chatterjee; Information Processing in Agriculture 2017-01-01
430 Searching Patterns for Relation Extraction Over The Web: Rediscovering The Pattern-relation Duality
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Abstract: While tuple extraction for a given relation has been an active research area, its dual problem of pattern search– to find and rank patterns in a principled way– has not been …		 Yuan Fang; Kevin Chen-Chuan Chang; 2011-01-01
431 Progressive Multi-task Learning with Controlled Information Flow for Joint Entity and Relation Extraction
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Highlight: To achieve this objective, in this paper we design a multitask learning architecture based on the observation that correlations exist between outputs of some related tasks (e.g. entity recognition and relation extraction tasks), and they reflect the relevant features that need to be extracted from the input.		 Kai Sun; Richong Zhang; Samuel Mensah; Yongyi Mao; Xudong Liu; aaai 2021-02-09
432 Self-Attention Enhanced CNNs And Collaborative Curriculum Learning For Distantly Supervised Relation Extraction
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Highlight: In this paper, we propose a novel model that employs a collaborative curriculum learning framework to reduce the effects of mislabelled data.		 Yuyun Huang; Jinhua Du; emnlp 2019-11-02
433 Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks
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Highlight: We empirically show that this graph-based approach quite effectively captures the relations between different entity pairs in a dialogue as it outperforms the state-of-the-art approaches by a significant margin on the benchmark dataset DialogRE.		 Hui Chen; Pengfei Hong; Wei Han; Navonil Majumder; Soujanya Poria; arxiv-cs.CL 2020-09-10
434 A Position Encoding Convolutional Neural Network Based On Dependency Tree For Relation Classification
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 Yunlun Yang; Yunhai Tong; Shulei Ma; Zhi-Hong Deng; emnlp 2016-12-30
435 How to Unleash The Power of Large Language Models for Few-shot Relation Extraction?
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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
436 Minimize Exposure Bias Of Seq2Seq Models In Joint Entity And Relation Extraction
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Highlight: We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets.		 RANRAN HAORAN ZHANG et. al. emnlp 2020-11-10
437 A Novel Feature-based Approach To Chinese Entity Relation Extraction
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 Wenjie Li; Peng Zhang; Furu Wei; Yuexian Hou; Qin Lu; acl 2008-12-30
438 RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search
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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
439 Global Distant Supervision For Relation Extraction
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Highlight: In this paper, we propose a global distant supervision model for relation extraction, which can: 1) compensate the lack of supervision with a wide variety of indirect supervision knowledge; and 2) reduce the uncertainty in DS by performing joint inference across relation instances.		 Xianpei Han; Le Sun; aaai 2016-02-12
440 Encrypted Traffic Classification Based on Gaussian Mixture Models and Hidden Markov Models
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 ZHONGJIANG YAO et. al. J. Netw. Comput. Appl. 2020-01-01
441 A Sequential Model For Classifying Temporal Relations Between Intra-Sentence Events
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Highlight: We present a sequential model for temporal relation classification between intra-sentence events.		 Prafulla Kumar Choubey; Ruihong Huang; emnlp 2017-11-07
442 Pre-training Entity Relation Encoder With Intra-span And Inter-span Information
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Highlight: In this paper, we integrate span-related information into pre-trained encoder for entity relation extraction task.		 YIJUN WANG et. al. emnlp 2020-11-12
443 MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction
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Highlight: In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction.		 Manqing Dong; Chunguang Pan; Zhipeng Luo; emnlp 2021-11-05
444 Chemical-protein Relation Extraction With Ensembles Of SVM, CNN, And RNN Models
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Highlight: This manuscript describes our submission, which is an ensemble of three systems, including a Support Vector Machine, a Convolutional Neural Network, and a Recurrent Neural Network.		 Yifan Peng; Anthony Rios; Ramakanth Kavuluru; Zhiyong Lu; arxiv-cs.CL 2018-02-04
445 Semantic Representations For Domain Adaptation: A Case Study On The Tree Kernel-based Method For Relation Extraction
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 Thien Huu Nguyen; Barbara Plank; Ralph Grishman; acl 2015-12-30
446 LOME: Large Ontology Multilingual Extraction
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Highlight: We present LOME, a system for performing multilingual information extraction.		 PATRICK XIA et. al. arxiv-cs.CL 2021-01-28
447 Jointly Extracting Relations With Class Ties Via Effective Deep Ranking
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Highlight: In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced.		 Hai Ye; Wenhan Chao; Zhunchen Luo; Zhoujun Li; arxiv-cs.AI 2016-12-22
448 Selecting Optimal Context Sentences for Event-Event Relation Extraction
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Highlight: In this work, we introduce a novel method to better model document-level context with important context sentences for event-event relation extraction.		 Hieu Man; Nghia Trung Ngo; Linh Ngo Van; Thien Huu Nguyen; aaai 2022-02-07
449 A Novel Document-Level Relation Extraction Method Based on BERT and Entity Information
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Abstract: Document-level relation extraction aims to extract the relationship among the entities in a paragraph of text. Compared with sentence-level, the text in document-level relation …		 Xiaoyu Han; Lei Wang; IEEE Access 2020-01-01
450 Video Relation Detection Via Multiple Hypothesis Association
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Highlight: In this paper, we propose a novel relation association method called Multiple Hypothesis Association (MHA).		 ZIXUAN SU et. al. mm 2020-10-12
451 Improving Distantly-Supervised Relation Extraction Through BERT-Based Label and Instance Embeddings
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Abstract: Distantly-supervised relation extraction (RE) is an effective method to scale RE to large corpora but suffers from noisy labels. Existing approaches try to alleviate noise through …		 Despina Christou; Grigorios Tsoumakas; IEEE Access 2021-01-01
452 Revisiting Unsupervised Relation Extraction
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Highlight: However, we demonstrate that by using only named entities to induce relation types, we can outperform existing methods on two popular datasets.		 Thy Thy Tran; Phong Le; Sophia Ananiadou; acl 2020-06-20
453 Spatial Template Extraction for Image Retrieval By Region Matching
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Abstract: This paper presents a template and its relation extraction and estimation (TREE) algorithm for indexing images from picture libraries with more semantics-sensitive meanings. This …		 Jun-Wei Hsieh; W. Eric L. Grimson; IEEE transactions on image processing : a publication of … 2003-01-01
454 Exploiting The Scope Of Negations And Heterogeneous Features For Relation Extraction: A Case Study For Drug-Drug Interaction Extraction
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 Md. Faisal Mahbub Chowdhury; Alberto Lavelli; naacl 2013-12-30
455 Static Relations: A Piece in The Biomedical Information Extraction Puzzle
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Abstract: We propose a static relation extraction task to complement biomedical information extraction approaches. We argue that static relations such as part-whole are implicitly involved …		 Sampo Pyysalo; Tomoko Ohta; Jin-Dong Kim; Jun’ichi Tsujii; 2009-01-01
456 Heterogeneous Graph Neural Networks for Noisy Few-shot Relation Classification
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 Yuxiang Xie; Hua Xu; Jiaoe Li; Congcong Yang; Kai Gao; Knowl. Based Syst. 2020-01-01
457 The Effects of Speech Recognition and Punctuation on Information Extraction Performance
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Abstract: We report on experiments to measure the effect of speech recognition errors and automatic punctuation insertion errors on the performance of information extraction (entity and …		 J. MAKHOUL et. al. Interspeech 2005-01-01
458 DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
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Highlight: We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.		 NINGYU ZHANG et. al. arxiv-cs.CL 2022-01-10
459 Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
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Highlight: In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level.		 YICHAO ZHOU et. al. aaai 2021-02-09
460 HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
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Highlight: Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning.		 SHULIANG LIU et. al. naacl 2022-07-09
461 Implementation of A Kernel-Based Chinese Relation Extraction System
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 Liu Kebin; Li Fang; Liu Lei; Han Ying; Journal of Computer Research and Development 2007-01-01
462 Relation Extraction From Biomedical And Clinical Text: Unified Multitask Learning Framework
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Highlight: In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction.		 Shweta Yadav; Srivatsa Ramesh; Sriparna Saha; Asif Ekbal; arxiv-cs.CL 2020-09-20
463 Exploiting The Syntax-Model Consistency For Neural Relation Extraction
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Highlight: In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization.		 Amir Pouran Ben Veyseh; Franck Dernoncourt; Dejing Dou; Thien Huu Nguyen; acl 2020-06-20
464 Semantic Rule Filtering for Web-Scale Relation Extraction
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Abstract: Web-scale relation extraction is a means for building and extending large repositories of formalized knowledge. This type of automated knowledge building requires a decent level …		 ANDREA MORO et. al. 2013-01-01
465 Logic-guided Semantic Representation Learning For Zero-Shot Relation Classification
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Highlight: In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification.		 JUAN LI et. al. arxiv-cs.CL 2020-10-30
466 Connecting Language And Knowledge With Heterogeneous Representations For Neural Relation Extraction
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Highlight: We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE.		 Peng Xu; Denilson Barbosa,; naacl 2019-06-02
467 Connecting Language And Knowledge With Heterogeneous Representations For Neural Relation Extraction
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Abstract: Knowledge Bases (KBs) require constant up-dating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the …		 Peng Xu; Denilson Barbosa; arxiv-cs.CL 2019-03-25
468 Towards Understanding Gender Bias In Relation Extraction
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Highlight: We create WikiGenderBias, a distantly supervised dataset with a human annotated test set.		 ANDREW GAUT et. al. arxiv-cs.LG 2019-11-09
469 Extracting Implicit Social Relation For Social Recommendation Techniques In User Rating Prediction
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Highlight: Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user.		 SEYED MOHAMMAD TAHERI et. al. arxiv-cs.SI 2016-12-05
470 Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification
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Highlight: To make full use of the existing information and get a better feature representation for each instance, we propose to encode each class prototype in an adaptive way from two aspects.		 Yan Xiao; Yaochu Jin; Kuangrong Hao; arxiv-cs.CL 2021-01-10
471 Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
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Highlight: To remedy this, we propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC, and apply it to the TACRED dataset.		 Ofer Sabo; Yanai Elazar; Yoav Goldberg; Ido Dagan; arxiv-cs.CL 2021-04-17
472 HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
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Highlight: Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning.		 XUMING HU et. al. arxiv-cs.CL 2022-05-04
473 A Comprehensive Exploration of Semantic Relation Extraction Via Pre-trained CNNs
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 Qing Li; Lili Li; Weinan Wang; Qi Li; Jiang Zhong; Knowl. Based Syst. 2020-01-01
474 Learning To Decouple Relations: Few-Shot Relation Classification With Entity-Guided Attention And Confusion-Aware Training
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Highlight: To alleviate the above relation confusion problem, we propose CTEG, a model equipped with two mechanisms to learn to decouple these easily-confused relations.		 YINGYAO WANG et. al. arxiv-cs.CL 2020-10-21
475 GeoLayoutLM: Geometric Pre-Training for Visual Information Extraction
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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
476 MIT At SemEval-2017 Task 10: Relation Extraction With Convolutional Neural Networks
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Highlight: In this work, we continue this line of work and present a system based on a convolutional neural network to extract relations.		 Ji Young Lee; Franck Dernoncourt; Peter Szolovits; arxiv-cs.CL 2017-04-05
477 Semantic Relation Extraction from Legislative Text Using Generalized Syntactic Dependencies and Support Vector Machines
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Abstract: In this paper we present a technique to automatically extract semantic knowledge from legislative text. Instead of using pattern matching methods relying on lexico-syntactic …		 Guido Boella; Luigi Di Caro; Livio Robaldo; 2013-01-01
478 Open Relation Extraction For Support Passage Retrieval: Merit And Open Issues
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Highlight: Our goal is to complement an entity ranking with human-readable explanations of how those retrieved entities are connected to the information need.		 Amina Kadry; Laura Dietz; sigir 2017-07-29
479 Denoising Relation Extraction From Document-level Distant Supervision
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Highlight: To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks.		 CHAOJUN XIAO et. al. emnlp 2020-11-12
480 Enhanced Prototypical Network for Few-shot Relation Extraction
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 Wen Wen; Yongbin Liu; Chunping Ouyang; Qiang Lin; Tong Lee Chung; Inf. Process. Manag. 2021-01-01
481 Extracting Relations from Text: From Word Sequences to Dependency Paths
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Abstract: Extracting semantic relationships between entities mentioned in text documents is an important task in natural language processing. The various types of relationships that are …		 Razvan Bunescu; Raymond J. Mooney; 2007-01-01
482 Collaborative Text-annotation Resource for Disease-centered Relation Extraction from Biomedical Text
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 Carlos Cano; Thomas Monaghan; Armando Blanco; Dennis P. Wall; Leonid Peshkin; Journal of biomedical informatics 2009-01-01
483 Personal Knowledge Graph Population from User Utterances in Conversational Understanding
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Abstract: Knowledge graphs provide a powerful representation of entities and the relationships between them, but automatically constructing such graphs from spoken language utterances …		 Xiang Li; Gökhan Tür; Dilek Z. Hakkani-Tür; Qi Li; 2014 IEEE Spoken Language Technology Workshop (SLT) 2014-01-01
484 Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents
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Abstract: The use of administrative documents to communicate and leave record of business information requires of methods able to automatically extract and understand the content from such …		 Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornés; Josep Lladós; 2020 25th International Conference on Pattern Recognition … 2021-01-01
485 A Review on Entity Relation Extraction
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Abstract: Because of large amounts of unstructured data generated on the Internet, entity relation extraction is believed to have high commercial value. Entity relation extraction is a case …		 Qianqian Zhang; Mengdong Chen; Lianzhong Liu; 2017 Second International Conference on Mechanical, Control … 2017-01-01
486 Downstream Model Design Of Pre-trained Language Model For Relation Extraction Task
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Highlight: In this paper, a new network architecture with a special loss function is designed to serve as a downstream model of PLMs for supervised relation extraction.		 Cheng Li; Ye Tian; arxiv-cs.CL 2020-04-07
487 Exploiting Entity BIO Tag Embeddings And Multi-task Learning For Relation Extraction With Imbalanced Data
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Highlight: To mitigate this problem, we propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss.		 WEI YE et. al. acl 2019-07-28
488 A Relation Extraction Method of Chinese Named Entities Based on Location and Semantic Features
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Abstract: Named entity relations are a foundation of semantic networks, ontology and the semantic Web, and are widely used in information retrieval and machine translation, as well as …		 Hai-Guang Li; Xindong Wu; Zhao Li; Gong-Qing Wu; Applied Intelligence 2009-01-01
489 Verb Metaphor Detection Via Contextual Relation Learning
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Highlight: In this paper, we argue that it is appropriate to view this task as relation classification between a verb and its various contexts.		 Wei Song; Shuhui Zhou; Ruiji Fu; Ting Liu; Lizhen Liu; acl 2021-07-26
490 Exploiting Entity BIO Tag Embeddings And Multi-task Learning For Relation Extraction With Imbalanced Data
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Highlight: To mitigate this problem, we propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss.		 WEI YE et. al. arxiv-cs.CL 2019-06-20
491 Domain Knowledge Empowered Structured Neural Net For End-to-End Event Temporal Relation Extraction
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Highlight: To address these issues, we propose a framework that enhances deep neural network with distributional constraints constructed by probabilistic domain knowledge.		 Rujun Han; Yichao Zhou; Nanyun Peng; emnlp 2020-11-12
492 Video Visual Relation Detection Via Iterative Inference
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Abstract: The core problem of video visual relation detection (VidVRD) lies in accurately classifying the relation triplets, which comprise of the classes of subject and object entities, …		 Xindi Shang; Yicong Li; Junbin Xiao; Wei Ji; Tat-Seng Chua; Proceedings of the 29th ACM International Conference on … 2021-01-01
493 Attentive Recurrent Neural Network for Weak-supervised Multi-label Image Classification
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Abstract: Multi-label image classification is a fundamental and challenging task in computer vision, and recently achieved significant progress by exploiting semantic relations among …		 Liang Li; Shuhui Wang; Shuqiang Jiang; Qingming Huang; Proceedings of the 26th ACM international conference on … 2018-01-01
494 Neural Metric Learning For Fast End-to-End Relation Extraction
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Highlight: We introduce a novel neural architecture utilizing the table structure, based on repeated applications of 2D convolutions for pooling local dependency and metric-based features, that improves on the state-of-the-art without the need for global optimization.		 Tung Tran; Ramakanth Kavuluru; arxiv-cs.CL 2019-05-17
495 Are Noisy Sentences Useless for Distant Supervised Relation Extraction?
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Highlight: Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences.		 Yuming Shang; He-Yan Huang; Xian-Ling Mao; Xin Sun; Wei Wei; aaai 2020-02-07
496 Dependency Tree Kernels for Relation Extraction from Natural Language Text
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Abstract: The automatic extraction of relations from unstructured natural text is challenging but offers practical solutions for many problems like automatic text understanding and semantic …		 Frank Reichartz; Hannes Korte; Gerhard Paass; 2009-01-01
497 Are Noisy Sentences Useless For Distant Supervised Relation Extraction?
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Highlight: Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences.		 Yuming Shang; arxiv-cs.CL 2019-11-21
498 A Sentence Simplification System For Improving Relation Extraction
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Highlight: In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems.		 Christina Niklaus; Bernhard Bermeitinger; Siegfried Handschuh; André Freitas; arxiv-cs.CL 2017-03-27
499 KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports
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Highlight: We present KPI-BERT, a system which employs novel methods of named entity recognition (NER) and relation extraction (RE) to extract and link key performance indicators (KPIs), e.g. revenue or interest expenses, of companies from real-world German financial documents.		 LARS HILLEBRAND et. al. arxiv-cs.CL 2022-08-03
500 Relation Extraction As Open-book Examination: Retrieval-enhanced Prompt Tuning
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Highlight: To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction.		 XIANG CHEN et. al. arxiv-cs.CL 2022-05-04

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