Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230900030-6.doi: 10.11896/jsjkx.230900030

• Artificial Intelligenc • Previous Articles     Next Articles

TCM Named Entity Recognition Model Combining BERT Model and Lexical Enhancement

LI Minzhe, YIN Jibin   

  1. Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China
  • Published:2024-06-06
  • About author:LI Minzhe,bornin 1997,postgraduate.His main research interests include deep learning and natural language processing.
    YIN Jibin,born in 1976,Ph.D,associate professor.His main research interests include human-computer interaction and artificial intelligence.

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Abstract: There are few researches on TCM named entity recognition,and most of them are based on Chinese medical cases,and they do not perform well in TCM case texts.Aiming at the characteristics of dense named entities and fuzzy boundary in TCM cases,this paper proposes a method of TCM named entity recognition,LEBERT-BILSTM-CRF,which combines lexical enhancement and pre-training model.This method is optimized from the perspective of the fusion of vocabulary enhancement and pre-training model,and the vocabulary information is input into the BERT model for feature learning,so as to achieve the purpose of dividing word class boundaries and distinguishing word class attributes,and improve the accuracy of TCM medical case named entity recognition.Experiments show that when ten entities are identified on the TCM case data set constructed in this paper,the comprehensive accuracy rate,recall rate and F1 of the TCM case named entity recognition model based on LEBERT-BILSTM-CRF is 88.69%,87.4% and 88.1%,respectively.It is higher than common named entity recognition models such as BERT-CRF and LEBERT-CRF.

Key words: Natural language processing, Chinese medicine case, Vocabulary enhancement, BERT, BiLSTM-CRF

CLC Number: 

  • TP391
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