基于句法语义增强的实体事件关系对联合抽取

doi:10.3969/j.issn.0255-8297.2025.06.011

摘要/Abstract

摘要： 针对事件描述边界模糊、句法语义信息利用率低的问题，提出了基于句法语义增强的实体事件关系对联合抽取模型。该方法充分考虑了实体和事件的上下文信息、时序信息和句法结构信息。首先，利用双向长短时记忆网络捕获文本的上下文信息和时序信息，生成语义更加丰富的嵌入表示；然后，设计了基于句法语义增强的实体事件关系对联合抽取模型，充分考虑文本的句法标签语义信息；最后，将实体事件关系对抽取和实体事件匹配两个子任务进行关联，实现实体事件关系对的联合抽取。实验结果表明，所提出的联合抽取方法在提高模型训练效率的同时，可以实现模型训练参数共享并减少错误标签传播，有效提高了实体事件关系对抽取的准确率。

关键词: 事件抽取, 实体事件匹配, 事件关系对抽取, 句法语义增强, 双向长短期记忆网络, 图卷积网络

Abstract: To address the issues of fuzzy event description boundaries and insufficient utilization of syntactic and semantic information, this paper proposes a syntactic-semantic-enhanced model for joint extraction of entity-event relationships. The proposed approach comprehensively incorporates contextual, temporal, and syntactic structural information of entities and events. Firstly, it utilizes bidirectional long short-term memory networks (BiLSTM) to capture the contextual and temporal information of the text, generating more semantically enriched embeddings. Secondly, a syntactic-semantic-enhanced model is designed for joint extraction of entity-event relationships, considering the syntactic label semantics in the text. Finally, it establishes a connection between the extraction of entity-event relationships and entity-event matching, achieving the joint extraction of entity-event relationships. Experimental results demonstrate that the proposed joint extraction method not only improves model training efficiency but also enables parameter sharing and reduces error label propagation, thereby enhancing the accuracy of entity-event relationship pair extraction.

Key words: event extraction, entity-event matching, event relation pair extraction, syntactic semantic enhancement, bidirectional long short-term memory network (BiLSTM), graph convolutional network (GCN)

中图分类号:

TP391

高剑奇, 黄典, 骆祥峰. 基于句法语义增强的实体事件关系对联合抽取[J]. 应用科学学报, 2025, 43(6): 1024-1036.

GAO Jianqi, HUANG Dian, LUO Xiangfeng. Entity-Event Relation Joint Extraction Enhanced with Syntactic Semantic[J]. Journal of Applied Sciences, 2025, 43(6): 1024-1036.

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