电子科技大学学报2024，Vol.53Issue(3)：431-439,9.DOI:10.12178/1001-0548.2023064
结合全局信息增强的医学领域命名实体识别研究
Research on Named Entity Recognition in Medical Domain with Global Information Augmentation
摘要
Abstract
Entities such as drug names are difficult to identify accurately in Chinese medical questioning texts due to the frequent occurrence of colloquial irregular expressions and jargon.To make full use of the important role of inter-word relations in Chinese sentences,a medical named entity recognition model for enhancing global information is proposed.The model enhances the word embedding representation using an attention mechanism and enriches the global information representation of sentences in two ways simultaneously,based on the use of the sequence processing capability of bidirectional long and short-term memory networks to obtain contextual information.Firstly,a graphical convolutional network layer is constructed to enrich inter-word dependencies based on syntactic relationships to obtain additional dependencies between words;secondly,an auxiliary task is constructed to predict the class of syntactic dependencies between words.Experimental results on the Chinese medical consultation dataset show that the model is very competitive,with an F1 value of 94.54%.Significant improvements are achieved in the recognition of entity classes such as drugs and symptoms compared to other models.Experiments on the Weibo public dataset also show that the model has general-domain applications.关键词
注意力机制/双向长短时记忆网络/图卷积网络/医疗问诊/命名实体识别Key words
attention mechanism/bidirectional long and short-term memory network/graph convolutional network/medical consultation/named entity recognition分类
信息技术与安全科学引用本文复制引用
要媛媛,付潇,杨东瑛,王洁宁,郑文..结合全局信息增强的医学领域命名实体识别研究[J].电子科技大学学报,2024,53(3):431-439,9.基金项目
国家自然科学基金(11702289) （11702289）
山西省关键核心技术和共性技术研发攻关专项(2020XXX013) （2020XXX013）
