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.
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
.
DOI: 10.3969/j.issn.0255-8297.2025.06.011
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