Entity Recognition of Medical Conversation Based on Word Fusion and Adversarial Training
Aiming at the problems of insufficient acquisition of word boundary features,weak semantic gen-eralization ability of entity boundaries and poor recognition accuracy of complex entities nested in the process of entity recognition of medical dialogue by BERT-BiLSTM-CR Chinese F,a medical dialogue entity recognition model based on word fusion and adversarial training is proposed.Firstly,introduce the word features correspond-ing to the characters in the external vocabulary matching sentences,integrate the word fusion vector into the BERT model through the Lexicon Adapter(LA),add adversarial training(Projected Gradient Descent,PGD)to generate adversarial samples,and then pass the word fusion vector and adversarial samples as training data to the bidirectional gated loop unit(Bidirectional Gated Recurrent Unit,BiGRU)extract the context semantic in-formation,and finally decode it with a Conditional Random Field(CRF).Experiments on the IMCS21 Chinese medical dialogue dataset show that the F1 value of the model reach 92.06%.Compared with the BERT-BiL-STM-CRF model,the entity understanding and label recognition accuracy of complex semantics are effectively improved.
NETL
NSTL
万方数据
