Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (06): 176-184.doi: 10.12052/gdutxb.230065

• Artifical Intelligence • Previous Articles    

Chinese Medical Named Entity Recognition Based on Gated Attention Unit

Wu Xiao-ling, Chen Xiang-wang, Zhan Wen-tao, Ling Jie   

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-05-06 Online:2023-11-25 Published:2023-11-08

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Abstract: The medical named entity recognition aims to automatically identify and classify medical entities in electronic medical records, which plays a very important role in downstream tasks such as information retrieval and knowledge graph. Existing methods usually ignore the dependencies between entities. To address this, this paper proposes a gated attention unit-based model for Chinese medical named entity recognition. First, the proposed model uses the pre-training model MC-BERT to capture contextual information. Then, it uses the cross-attention and gated attention unit to enhance the interaction between entity query and contextual semantics, and further extract the dependency and correlation between entities. Finally, the proposed model uses the matching algorithm of bipartite graph to calculate the loss. This paper conducted experiments on three datasets, including the CMeEE, CMQNN, and MSRA. The experimental results show that the F1 values of the proposed model on the three datasets are 70.74%, 96.92%, and 95.53%, respectively, which outperforms other related models, demonstrating the effectiveness of the proposed model in Chinese medical named entity recognition task.

Key words: Chinese named entity recognition, gated attention unit, cross attention, MC-BERT

CLC Number: 

  • TP391.41
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