关键词: 电子病历（EMR）, 命名实体识别, Transformer, 条件随机场（CRF）

Abstract:

Named entity recognition is one of the basic tasks of natural language processing. Aiming at the problem that the traditional model of Chinese EMR named entity recognition is not effective, a neural network model based on attention mechanism is proposed. Firstly, the experiment uses self-built real Chinese electronic medical record data sets and preprocesses the data sets by manual labeling and word segmentation. Secondly, it trains optimization of Transformer model to extract text features. Finally, it uses conditional random fields to classify and recognize the extracted text features. To verify the effectiveness of the proposed method, the Transformer-CRF neural network model is compared with seven other traditional models. The recognition performance of the model is evaluated by three indicators： precision, recall and F1 value. The experimental results show that in the same corpus, the transformer-CRF model has a better recognition effect on the named entity of Body parts, and the F1 value is as high as 95.02%, and compared with the other seven traditional models, the precision, recall and F1 value of the transformer-CRF model are higher, which proves that the model has a better recognition performance in a certain degree.

Key words: Electronic Medical Records（EMR）, named entity recognition, Transformer, Conditional Random Fields（CRF）