计算机工程与应用2024，Vol.60Issue(11)：147-155,9.DOI:10.3778/j.issn.1002-8331.2302-0321
融合词信息和图注意力的医学命名实体识别
Medical Named Entity Recognition Incorporating Word Information and Graph Attention
摘要
Abstract
The Chinese clinical natural language is rich in a large amount of medical record information.Naming entity recognition for electronic medical records can help establish medical auxiliary diagnostic systems,which is of great signif-icance for the development of the medical field.At the same time,it is conducive to downstream tasks such as relationship extraction and the implementation of knowledge graphs.However,Chinese electronic medical records have problems with difficulty in Chinese word segmentation,numerous medical terminology,and special expressions,which can easily lead to incorrect expression of text features.Therefore,this paper proposes a medical named entity recognition research model based on enhanced word information and graph attention,which improves the performance of the network model by enhancing local and global features.Due to the fact that embedding a single word vector for Chinese entity recognition can easily ignore word information and semantics in the text,this paper embeds a highly correlated word vector in the word vector,which not only enhances text representation but also avoids word segmentation errors.Additionally,a Med-Bert model for learning medical knowledge is embedded in the word embedding layer,which can dynamically generate feature vectors according to different contexts,helps solve the problem of polysemy and specialized vocabulary in elec-tronic medical records.At the same time,adding a graph attention module in the coding layer enhances the network's abil-ity to learn text context relationships and enhances the model's learning of medical special grammar.Finally,F1 values of 86.38%and 84.76%are obtained on the cEHRNER and cMedQANER datasets,respectively,showing better robustness compared to other models.关键词
图注意力/匹配词/命名实体识别/Bert模型Key words
graph attention networks/word embedding/named entity recognition/Bert model分类
信息技术与安全科学引用本文复制引用
赵珍珍,董彦如,刘静,张俊忠,曹慧..融合词信息和图注意力的医学命名实体识别[J].计算机工程与应用,2024,60(11):147-155,9.基金项目
国家自然科学基金(82074579,81973981,8217452) （82074579,81973981,8217452）
山东省自然科学基金(ZR2020MH360). （ZR2020MH360）
