基于上下文融合的文档级事件抽取方法  

Document level event extraction method based on context fusion

作　　者：葛君伟[1] 乔蒙蒙 方义秋[1] Ge Junwei;Qiao Mengmeng;Fang Yiqiu(College of Computer Science&Technology,Chongqing University of Posts&Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学计算机科学与技术学院,重庆400065

出　　处：《计算机应用研究》2022年第1期48-53,共6页Application Research of Computers

基　　金：国家自然科学基金面上项目(62072066)。

摘　　要：基于句子级别的抽取方法不足以解决中文事件元素分散问题。针对该问题,提出基于上下文融合的文档级事件抽取方法。首先将文档分割为多个段落,利用双向长短期记忆网络提取段落序列特征;其次采用自注意力机制捕获段落上下文的交互信息;然后与文档序列特征融合以更新语义表示;最后采用序列标注方式抽取事件元素并匹配事件类型。与其他事件抽取方法在相同的中文数据集上进行对比,实验结果表明,该方法能有效抽取文档中分散的事件元素,并提升模型的抽取性能。The sentence level extraction method is insufficient to solve the problem of Chinese event element dispersion.To solve this problem,this paper proposed a document level event extraction method based on context fusion.Firstly,the paper divided the document into paragraphs,and used bidirectional long and short memory network to extract sequence features of paragraphs.Secondly,the method used self-attention mechanism to capture the interaction information of paragraph context.Then the method combined the document sequence features with the interaction information to update the semantic representation.Finally,the method used sequence annotation to extract event elements and match event types.Compared with other event extraction methods on the same Chinese data set,the experimental results show that this method can effectively extract scattered event elements from documents,and improve the extraction performance of the model.

关 键 词：事件抽取 序列标注 特征提取 事件元素 上下文融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]