基于多粒度阅读器和图注意力网络的文档级事件抽取  

Document level event extraction based on multi granularityreaders and graph attention networks

作　　者：薛颂东[1] 李永豪 赵红燕[1] Xue Songdong;Li Yonghao;Zhao Hongyan(School of Computer Science&Technology,Taiyuan University of Science&Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学计算机科学与技术学院,太原030024

出　　处：《计算机应用研究》2024年第8期2329-2335,共7页Application Research of Computers

基　　金：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资助项目(CICIP2022004);太原科技大学博士科研启动基金资助项目(20212075)。

摘　　要：文档级事件抽取面临论元分散和多事件两大挑战,已有工作大多采用逐句抽取候选论元的方式,难以建模跨句的上下文信息。为此,提出了一种基于多粒度阅读器和图注意网络的文档级事件抽取模型,采用多粒度阅读器实现多层次语义编码,通过图注意力网络捕获实体对之间的局部和全局关系,构建基于实体对相似度的剪枝完全图作为伪触发器,全面捕捉文档中的事件和论元。在公共数据集ChFinAnn和DuEE-Fin上进行了实验,结果表明提出的方法改善了论元分散问题,提升了模型事件抽取性能。Document level event extraction faces two major challenges:argument dispersion and multiple events.Most exis-ting work adopts the method of extracting candidate arguments sentence by sentence,which makes it difficult to model contextual information across sentences.Therefore,this paper proposed a document level event extraction model based on multi granularity readers and graph attention networks.It used multi-granularity readers to achieve multi-level semantic encoding,and used the graph attention network to capture local and global relations between entity pairs.It constructed a pruned complete graph based on entity pair similarity as a pseudo trigger to comprehensively capture events and arguments in the document.Experiments conducted on the public datasets of ChFinAnn and DuEE-Fin show that the proposed method improves the problem of argument dispersion and enhances model’s event extraction performance.

关 键 词：多粒度阅读器 图注意力网络 文档级事件抽取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]