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A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

Wei Liu, Meijuan Yin*, Jialong Zhang, Lunchong Cui

Key Laboratory of Cyberspace Situation Awareness of Henan Province, Zhengzhou, 450001, China

* Corresponding Author: Meijuan Yin. Email: email

Computers, Materials & Continua 2024, 78(1), 975-997. https://doi.org/10.32604/cmc.2023.046475

Abstract

The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities, and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation. However, this method has some problems, such as relying on expert experience and poor portability. Inspired by the rule-based entity relation extraction method, this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed, which is abbreviated as RSTAC. This model refines the extraction rules of relation semantic templates from relation corpus through dependency parsing and realizes the automatic construction of relation semantic templates. Based on the relation semantic template, the process of relation classification and triplet extraction is constrained, and finally, the entity relation triplet is obtained. The experimental results on the three major Chinese datasets of DuIE, SanWen, and FinRE show that the RSTAC model successfully obtains rich deep semantics of relation, improves the extraction effect of entity relation triples, and the F1 scores are increased by an average of 0.96% compared with classical joint extraction models such as CasRel, TPLinker, and RFBFN.

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Cite This Article

W. Liu, M. Yin, J. Zhang and L. Cui, "A joint entity relation extraction model based on relation semantic template automatically constructed," Computers, Materials & Continua, vol. 78, no.1, pp. 975–997, 2024.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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