MedRelNet:a joint model for entity-relation extraction in Chinese medical text based on relation fusion
To address the issue of traditional entity-relation extraction models performing poorly on Chinese medical texts,the MedRelNet model based on Onerel is proposed.The core of MedRelNet is a novel relation fusion module(RelFuse),which inte-grates relation information into sentence representations to enable thorough interactions between entities and relations.Addition-ally,it incorporates bidirectional Long Short-Term Memory networks(BiLSTM)to comprehensively capture sentence features.Ex-perimental results show that MedRelNet improves F1 scores by 1,0.7,and 0.8 percentage point over baseline models on the CMeIE,DuIE,and WebNLG datasets,especially excelling in handling complex scenarios involving multiple relations and entity overlaps.
BiLSTMChinese medical textentity-relation extractionknowledge graph
万方数据
