research-article

Learning Discriminative Neural Representations for Event Detection

Authors:

Lingling Zhang,

Jiuyang TangAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 644 - 653

https://doi.org/10.1145/3404835.3462977

Published: 11 July 2021 Publication History

Abstract

Retrieving event instances from texts is pivotal to various natural language processing applications (e.g., automatic question answering and dialogue systems), and the first task to perform is event detection. There are two related sub-tasks therein-trigger identification and type classification, and the former is considered to play a dominant role. Nevertheless, it is notoriously challenging to predict event triggers right. To handle the task, existing work has made tremendous progress by incorporating manual features, data augmentation and neural networks, etc. Due to the scarcity of data and insufficient representation of trigger words, however, they still fail to precisely determine the spans of triggers (coined as trigger span detection problem). To address the challenge, we propose to learn discriminative neural representations (DNR) from texts. Specifically, our DNR model tackles the trigger span detection problem by exploiting two novel techniques: 1) a contrastive learning strategy, which enlarges the discrepancy between representations of words inside and outside triggers; and 2) a Mixspan strategy, which better trains the model to differentiate words nearby triggers' span boundaries. Extensive experiments on benchmarks-ACE2005 and TAC2015-demonstrate the superiority of our DNR model, leading to state-of-the-art performance.

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Cited By

Wan QWan CXiao KXiong HLiu DLiu XHu R(2024)Token-Event-Role Structure-Based Multi-Channel Document-Level Event ExtractionACM Transactions on Information Systems10.1145/364388542:4(1-27)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3643885
Li QLi JSheng JCui SWu JHei YPeng HGuo SWang LBeheshti AYu P(2024)A Survey on Deep Learning Event Extraction: Approaches and ApplicationsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321316835:5(6301-6321)Online publication date: May-2024
https://doi.org/10.1109/TNNLS.2022.3213168
Lin HYang HLuo ZMa J(2024)Unleashing Trigger-Free Event Detection: Revealing Event Correlations Via a Contrastive Derangement FrameworkICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448190(10171-10175)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448190
Show More Cited By

Index Terms

Learning Discriminative Neural Representations for Event Detection
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

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Publication History

Published: 11 July 2021

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National Nature Science Foundation of China
National Nature Science Foundation of China
National Science Foundation of Hunan Province
National Nature Science Foundation of China

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SIGIR '21

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SIGIR

SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Wan QWan CXiao KXiong HLiu DLiu XHu R(2024)Token-Event-Role Structure-Based Multi-Channel Document-Level Event ExtractionACM Transactions on Information Systems10.1145/364388542:4(1-27)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3643885
Li QLi JSheng JCui SWu JHei YPeng HGuo SWang LBeheshti AYu P(2024)A Survey on Deep Learning Event Extraction: Approaches and ApplicationsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321316835:5(6301-6321)Online publication date: May-2024
https://doi.org/10.1109/TNNLS.2022.3213168
Lin HYang HLuo ZMa J(2024)Unleashing Trigger-Free Event Detection: Revealing Event Correlations Via a Contrastive Derangement FrameworkICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448190(10171-10175)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448190
Wan QWan CXiao KHu RLiu D(2023)A Multi-channel Hierarchical Graph Attention Network for Open Event ExtractionACM Transactions on Information Systems10.1145/352866841:1(1-27)Online publication date: 10-Jan-2023
https://dl.acm.org/doi/10.1145/3528668
Song XJing LLin DZhao ZChen HNie LAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)V2PProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532076(992-1001)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532076
Liao JZhao XZheng JLi XCai FTang JAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)PTAUProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532048(1219-1229)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532048
Sheng JSun RGuo SCui SCao JWang LLiu TXu HAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)CorEDProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531956(1122-1132)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531956
Zhang QZhou JChen QBai QHe LAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Enhancing Event-Level Sentiment Analysis with Structured ArgumentsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531784(1944-1949)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531784

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