Medical Name Entity Recognition Based on Lexical Enhancement and Global Pointer

Abstract

Named entity recognition (NER) in biological sources, also called medical named entity recognition (MNER), attempts to identify and categorize medical terminology in electronic records. Deep neural networks have recently demonstrated substantial effectiveness in MNER. However, Chinese MNER has issues that cannot use lexical information and involve nested entities. To address these problems, we propose a model which can handle both nested and non-nested entities. The model uses a simple lexical enhancement method for merging lexical information into each character's vector representation, and then uses the Global Pointer approach for entity recognition. Furthermore, we retrain a pre-trained model with a Chinese medical corpus to incorporate medical knowledge, resulting in F1 score of 68.13% on the nested dataset CMeEE, 95.56% on the non-nested dataset CCKS2017, 85.89% on CCKS2019, and 92.08% on CCKS2020. These data demonstrate the efficacy of our proposed model.

Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer

“…In the comparative experiments on the CMeEE dataset, compared to the FLR-MRC [38] model, the F1 score of our model improved by 3.097%. Compared to the GPa+RoBERTa-large+SoftLexicon [39] model, the F1 score of our model improved by 1.487%. Compared to TPORE, the F1 score improved by 4.677%.…”