基于联合学习的语言粒度融合的重叠事件抽取方法  

Overlap Event Extraction Method with Language Granularity Fusion Based on Joint Learning

作　　者：闫婧涛 李旸[2] 王素格[1,3] 潘邦泽 YAN Jingtao;LI Yang;WANG Suge;PAN Bangze(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;School of Finance,Shanxi University of Finance and Economics,Taiyuan 030006,China;Key Laboratory Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan 030006,China)

机构地区：[1]山西大学计算机与信息技术学院,太原030006 [2]山西财经大学金融学院,太原030006 [3]山西大学计算智能与中文信息处理教育部重点实验室,太原030006

出　　处：《计算机科学》2024年第7期287-295,共9页Computer Science

基　　金：国家重点研发计划(2022QY0300-01);国家自然科学基金(62106130);山西省高等学校科技创新项目(2021L284)。

摘　　要：事件抽取是一项重要的信息抽取任务,现有的事件抽取方法大多假设一个句子中仅出现一个事件,然而,在真实的场景下,重叠事件是难以避免的。文中提出了一种基于联合学习的语言粒度融合的重叠事件抽取方法。该方法设计了基于token数目逐层递增和逐层递减的策略,对不同语言粒度的片段进行表示,在此基础上,构建了渐进式语言粒度融合的句子表示。通过引入事件信息感知,建立了基于门控机制的语言粒度和事件信息融合的句子表示。最后,通过联合学习词间的片段关系和角色关系,实现对事件触发词、论元、事件类型和论元角色的判别。在FewFC和DuEE1.0-1数据集上进行了实验,所提LGFEE模型在事件类型判别任务上的F1值分别提高了0.8%和0.6%,在触发词识别、论元识别、论元角色分类任务上也获得了较高的召回率和F1值,验证了其有效性。Event extraction is a crucial task in information extraction.The existing event extraction methods generally assume that only one event occurs in a sentence.However,overlapping events are inevitable in real scenarios.Therefore,this paper designs an overlap event extraction method with language granularity fusion based on joint learning.In this method,a strategy of increasing and decreasing token number layer by layer is designed to represent fragments of different language granularity.On this basis,a sentence representation of progressive language granularity fusion is constructed.By introducing event information perception,the sentence representation of language granularity and event information fusion based on gating mechanism is established.Finally,through the joint study of the fragment relationship and role relationship between words,the identification of event triggering words,arguments,event types and argument roles is realized.The experiments conducted on the FewFC and DuEE1.0-1 datasets demonstrate that the LGFEE model proposed in this paper achieves an improvement of 0.8%and 0.6%in the F1 score for event type discrimination tasks,respectively.Furthermore,it also exhibits higher recall rates and F1 scores in trigger word recognition,argument recognition,and argument role classification tasks,which verifies the validity of LGFEE model.

关 键 词：重叠事件抽取 语言粒度融合 联合学习 注意力机制 门控机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]