Document level event extraction based on multi granularity readers and graph attention networks

Xue Songdong
Li Yonghao
Zhao Hongyan
School of Computer Science & Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China

Abstract

Document level event extraction faces two major challenges: argument dispersion and multiple events. Most existing work adopts the method of extracting candidate arguments sentence by sentence, which makes it difficult to model contextual information across sentences. Therefore, this paper proposes a document level event extraction model based on multi granularity readers and graph attention networks. Multi granularity readers are used to achieve multi-level semantic encoding, and the graph attention network captures local and global relations between entity pairs. A pruned complete graph based on entity pair similarity is constructed as a pseudo trigger to comprehensively capture events and arguments in the document. Experiments were conducted on the public datasets Chfinance and DuEE-Fin, and the results showed that the proposed method improved the problem of argument dispersion and enhanced model event extraction performance.

Foundation Support

山西省基础研究计划资助项目(202203021211199)
智能信息处理山西省重点实验室开放基金项目(CICIP2022004)
太原科技大学博士科研启动基金项目(20212075)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0001
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-03-07] Accepted Paper

Cite This Article

薛颂东, 李永豪, 赵红燕. 基于多粒度阅读器和图注意力网络的文档级事件抽取 [J]. 计算机应用研究, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0001. (Xue Songdong, Li Yonghao, Zhao Hongyan. Document level event extraction based on multi granularity readers and graph attention networks [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0001. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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