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

作　　者：Wei Liu Meijuan Yin Jialong Zhang Lunchong Cui 

机构地区：[1]Key Laboratory of Cyberspace Situation Awareness of Henan Province,Zhengzhou,450001,China

出　　处：《Computers, Materials & Continua》2024年第1期975-997,共23页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China(Nos.U1804263,U1736214,62172435);the Zhongyuan Science and Technology Innovation Leading Talent Project(No.214200510019).

摘　　要：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 showthat 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.

关 键 词：Natural language processing deep learning information extraction relation extraction relation semantic template 

分 类 号：O64[理学—物理化学]