计算机工程2025，Vol.51Issue(9)：91-100,10.DOI:10.19678/j.issn.1000-3428.0069347
动态异构图增强的级联解码事件抽取
Event Extraction via Cascade Decoding Enhanced by Dynamic Heterogeneous Graphs
郭新宇 1马博 1艾比布拉·阿塔伍拉 1杨奉毅 1周喜1
作者信息
- 1. 中国科学院新疆理化技术研究所,新疆乌鲁木齐 830011||中国科学院大学,北京 100049||新疆民族语音语言信息处理实验室,新疆乌鲁木齐 830011
- 折叠
摘要
Abstract
Event extraction is an important information extraction task that aims to extract specific events or information from natural language texts.There are many overlapping event problems,where one word is used as a trigger for different event types or when event arguments for different roles in real-life event extraction scenarios.However,existing overlapping event extraction methods ignore the correlations and dependencies between event elements,such as event types and argument roles,resulting in a poor performance of overlapping event extraction.To solve this problem,this paper proposes an event extraction model via cascade decoding enhanced by dynamic heterogeneous graphs,named DHG-EE,which can effectively realize the structural representation of overlapping events and facilitates information transmission between event elements through a multi-granularity cascade decoding structure and a domain-event type-argument role heterogeneous graph network.First,the pre-trained model encodes the natural language text and constructs a multi-granularity heterogeneous graph network composed of domains,event types,and argument roles,which separates the overlapping event arguments from the corresponding multiple domain nodes and event-type nodes and efficiently represents the complex associations of overlapping events through the dynamic point-edge structure of the heterogeneous graph.Then,the multi-granularity cascading decoding structure decodes domain attributes,event types,event trigger words,and event arguments,in order from coarse to fine,according to semantic granularity and uses the information of the previous granularity as additional information to assist in the decoding of the next granularity.Experimental results show that the F1 value of the proposed model is better than that of the baseline models on the FewFC and DuEE1.0 benchmark event extraction datasets.关键词
信息抽取/事件抽取/重叠事件/异构图网络/级联解码Key words
information extraction/event extraction/overlapping event/heterogeneous graph network/cascade decoding分类
信息技术与安全科学引用本文复制引用
郭新宇,马博,艾比布拉·阿塔伍拉,杨奉毅,周喜..动态异构图增强的级联解码事件抽取[J].计算机工程,2025,51(9):91-100,10.
