Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (9): 287-294.doi: 10.11896/jsjkx.220900226

• Artificial Intelligence • Previous Articles     Next Articles

Chinese Medical Named Entity Recognition Method Incorporating Machine ReadingComprehension

LUO Yuanyuan1, YANG Chunming1,3, LI Bo1, ZHANG Hui2, ZHAO Xujian1,3   

  1. 1 School of Computer Science and Technology,Southwest University of Science and Technology,Mianyang,Sichuan 621000,China
    2 School of Mathematics and Physics,Southwest University of Science and Technology,Mianyang,Sichuan 621000,China
    3 Sichuan Big Data and Intelligent System Engineering Technology Research Center,Mianyang,Sichuan 621010,China
  • Received:2022-09-23 Revised:2022-12-02 Online:2023-09-15 Published:2023-09-01
  • About author:LUO Yuanyuan,born in 1998,postgra-duate,is a member of China Computer Federation.Her main research interests include knowledge graphs and natural language processing.
    YANG Chunming,born in 1980,asso-ciate professor,is a member of China Computer Federation.His main research interests include nature language processing and machine learning.
  • Supported by:
    Key R & D Project of Science & Technology Department of Sichuan Province(2021YFG0031) and Scientific and Technological Achievements Transformation Project of Sichuan Provincial Scientific Research Institute(22YSZH0021).

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Abstract: Medical named entity recognition is the key to automatically build a large-scale medical knowledge base.However,medical entities are often nested,and it can not be recognized by the sequence labeling method.This paper proposes a Chinese medical named entity recognition method based on reading comprehension framework.It models the nested named entity recognition problem as a machine reading problem,uses BERT to establish the connection between the reading comprehension problem and medical text,and introduces a multi-head attention mechanism to strengthen the semantic connection between the problem and nested named entity,and finally uses two classifiers to predict the beginning and end positions of entities.This method achieves the best results with an F1-score of 67.65% when compared with the current five mainstream methods.Compared with the most classical BiLSTM-CRF,the F1-score improves by 7.17%,and the nested “symptom” entities increase by 16.81%.

Key words: Named entity recognition, Chinese medical, Nested entities, Machine reading comprehension, Multi-head attention mechanism

CLC Number: 

  • TP391.1
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