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View all- 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
Token-Event-Role Structure-Based Multi-Channel Document-Level Event Extraction
Authors: 
Qizhi Wan, Changxuan Wan, Keli Xiao, Hui Xiong, Dexi Liu, Xiping Liu, and Rong HuAuthors Info & Claims
Qizhi Wan, Changxuan Wan, Keli Xiao, Hui Xiong, Dexi Liu, Xiping Liu, and Rong HuAuthors Info & ClaimsArticle No.: 104, Pages 1 - 27
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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Index Terms
- Token-Event-Role Structure-Based Multi-Channel Document-Level Event Extraction
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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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View all- 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
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