Research on named entity recognition of Chinese electronic medical records
When dealing with the text analysis of Chinese electronic medical records,we are faced with the challenges of polysemy and incomplete recognition.Therefore,a deep learning frame-work combining RoBERTa-WWM model and BiLSTM-CRF module is constructed.First,the pre-trained RoBERTa-WWM language model is deeply integrated with the semantic features generated by the Transformer layer to capture complex contextual information of the text.Then,the fusion semantic representation is input into BiLSTM and CRF modules to further re-fine the identification range and accuracy of entities.Finally,an empirical analysis was carried out on the CCKS2019 dataset,and the value was as high as 82.94％.This data strongly confirms the superior performance of RoBERTa-WWM-BiLSTM-CRF model in the recognition of named entities in Chinese electronic medical records.
RoBERTa-WWM modelChinese electronic medical recordsentity recognition
NETL
NSTL
万方数据
