research-article

Token-Event-Role Structure-Based Multi-Channel Document-Level Event Extraction

Authors:

Rong HuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 4

Article No.: 104, Pages 1 - 27

https://doi.org/10.1145/3643885

Published: 22 March 2024 Publication History

Abstract

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This article introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multi-channel prediction module, we transform entity and multi-event extraction into a single task of predicting token–event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework.

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Cited By

Wan QWan CXiao KHu RLiu DLiao GLiu XShuai Y(2024)A Multifocal Graph-Based Neural Network Scheme for Topic Event ExtractionACM Transactions on Information Systems10.1145/369635343:1(1-36)Online publication date: 19-Sep-2024
https://dl.acm.org/doi/10.1145/3696353
Kong JYang Z(2024)Recurrent event query decoder for document-level event extractionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108533133(108533)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108533

Index Terms

Token-Event-Role Structure-Based Multi-Channel Document-Level Event Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 4

July 2024

751 pages

EISSN:1558-2868

DOI:10.1145/3613639

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 22 March 2024

Online AM: 07 February 2024

Accepted: 20 January 2024

Revised: 02 December 2023

Received: 30 July 2023

Published in TOIS Volume 42, Issue 4

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National Natural Science Foundation of China
Guangzhou-HKUST(GZ) Joint Funding Program
Natural Science Foundation of Jiangxi Province
Funding Program for Academic and Technical Leaders in Major Disciplines of Jiangxi Province
Education Bureau of Guangzhou Municipality
Guangdong Science and Technology Department

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Cited By

Wan QWan CXiao KHu RLiu DLiao GLiu XShuai Y(2024)A Multifocal Graph-Based Neural Network Scheme for Topic Event ExtractionACM Transactions on Information Systems10.1145/369635343:1(1-36)Online publication date: 19-Sep-2024
https://dl.acm.org/doi/10.1145/3696353
Kong JYang Z(2024)Recurrent event query decoder for document-level event extractionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108533133(108533)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108533

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