基于令牌-事件-角色结构的多通道文档级事件提取

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Qizhi Wan, Changxuan Wan, Keli Xiao, Hui Xiong, Dexi Liu, Xiping Liu, Rong Hu
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引用次数: 0

摘要

文档级事件提取是一个长期存在的具有挑战性的信息检索问题,涉及一系列子任务:实体提取、事件类型判断和特定事件类型的多事件提取。然而,将该问题作为多个学习任务来处理会增加模型的复杂性。此外,现有方法没有充分利用跨越不同事件的实体之间的相关性,导致事件提取性能有限。本文介绍了一种用于文档级事件提取的新框架,其中包含一种名为 "标记-事件-角色 "的新数据结构和一个多通道参数角色预测模块。所提出的数据结构使我们的模型能够揭示标记在多个事件中的主要作用,从而有助于更全面地理解事件关系。通过利用多通道预测模块,我们将实体和多事件提取转化为预测标记-事件对的单一任务,从而减少了整体参数大小,提高了模型效率。结果表明,我们的方法在 F1 分数上比最先进的方法高出 9.5 个百分点,突出了其在事件提取方面的卓越性能。此外,一项消融研究证实了所提出的数据结构在改进事件提取任务方面的重要价值,进一步验证了它在提高框架整体性能方面的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction

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 paper 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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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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