Incorporating Graph Attention Network and Syntax for Medical Entity Identification
The electronic medical record data contains a large number of medical entities,and the automatic recogni-tion of these entities is beneficial for improving the understanding of electronic medical records.The electronic medi-cal record data contains professional medical terms and a large number of non-standard medical vocabulary.Rare words,long difficult words and omission in medical records bring challenges to medical entity recognition.To solve this problem,this paper proposes a medical entity recognition method based on graph attention network and syntax fusion.This method combines the co-occurrence relationship between words and the rules of syntactic dependency,and it realizes the fusion of various graph information by graph-attention network based on the construction of inter-active character-word relationship graph and dependency relationship graph of electronic medical record data.The experiment result reveals that the proposed method achieves 88.91％F1 value,which is 1.04％higher than the baseline model.
electronic medical recordnamed entity recognitiongraph attention network
万方数据
维普
