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Data Analysis and Knowledge Discovery
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Chinese Electronic Medical Record Named Entity Recognition Based on RoBERTa-wwm Dynamic Fusion Model
Zhang Yunqiu,Wang Yang,Li Bocheng
(School of Public Health, Jilin University, Changchun, 130021, China)
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[Objective]An entity recognition model based on RoBERTa-wwm dynamic fusion is proposed to improve the entity recognition effect of Chinese electronic medical records.

[Methods]After dynamicly fusing the semantic representations generated by each Transformer layer of the pre-trained language model RoBERTa-wwm,it’s inputed to the bi-directional long short-term memory network and the conditional random field module to complete the entity recognition in the electronic medical record.

[Results]In the "2017 National Knowledge Graph and Semantic Computing Conference (CCKS 2017)" data set and self-annotated electronic medical record data set,we get F1 values of 94.08% and 90.08% respectively. Compared to the RoBERTa-wwm-BiLSTM-CRF model, it improves

0.23% and 0.39%.

[Limitations]The RoBERTa-wwm used in this article completes the pre-training process based on non-medical corpus.

[Conclusions]The dynamic fusion of the semantic layer can make better use of the different information of each coding layer, and improve the effect of downstream entity recognition tasks.

Key words electronic medical record      named entity recognition      RoBERTa-wwm      dynamic fusion      
Published: 07 January 2022
ZTFLH:  TP391  

Cite this article:

Zhang Yunqiu, Wang Yang, Li Bocheng. Chinese Electronic Medical Record Named Entity Recognition Based on RoBERTa-wwm Dynamic Fusion Model . Data Analysis and Knowledge Discovery, 0, (): 1-.

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