Named Entity Recognition Model of Chinese Clinical Electronic Medical Record Based on XLNet-BiLSTM

Abstract The recognition of named entities in Chinese clinical electronic medical records is one of the basic tasks to realize smart medical care. Aiming at the insufficient text semantic representation of the traditional word vector model and the inability of the recurrent neural network (RNN) model to solve the problems of long-term dependence, a Chinese clinical electronic medical record named entity recognition model XLNet-BiLSTM-MHA-CRF based on XLNet is proposed. Use the XLNet pre-training language model as the embedding layer to vectorize the medical record text to solve the problem of ambiguity; use the bidirectional long and short-term memory network (BiLSTM) gate control unit to obtain the forward and backward semantic feature information of the sentence; Then input the feature sequence to the multi-head attention layer (multi-head attention, MHA), use MHA to obtain information represented by different subspaces of the feature sequence, enhance the relevance of context semantics and eliminate noise; finally, input the conditional random field CRF to identify the global maximum 优 sequence. The experimental results show that the XLNet-BiLSTM-Attention-CRF model has achieved good results on the CCKS-2017 named entity recognition data set.