基于异构图注意力网络的药物不良反应实体关系联合抽取研究OA北大核心CHSSCDCSSCICSTPCD

[Purpose/Significance]Joint extraction of entities and relations is a crucial component in the adverse drug reactions monitoring and knowledge organization.To address the issues of error propagation,entity redundancy and interac-tion deficiency in traditional pipeline extraction methods,and to improve the extraction effect of overlapping ternary groups of adverse drug reactions,the paper proposes a joint extraction model of adverse drug reactions entities and relations based on heterogeneous graph attention network MF-HGAT.[Method/Process]Firstly,the paper conducted knowledge trans-fered from external medical corpus resources through pre-training with BERT to achieve the fusion of multiple semantic fea-tures.Secondly,the paper introduced relations information as prior knowledge for heterogeneous graph nodes to avoid ex-tracting semantically irrelevant entities.Then,the paper enhanced the representations of characters and relations nodes by iteratively fusing messages with a hierarchical graph attention network through message passing.Finally,the paper extracted drug adverse reactions entities and relations after updating the node representations.[Result/Conclusion]Experiments on self-constructed adverse drug reactions datasets reveal that the joint extraction F1 value of MF-HGAT,which incorporates relations information and external medical and health domain knowledge,reaches 92.75%,which is an improvement of 5.29%over the mainstream model CasRel.The results demonstrate that the MF-HGAT model further enriches entity-rela-tions semantic information by fusing character and relations node semantics through heterogeneous graph attention network,which is of great significance to the knowledge discovery of adverse drug reactions.