Named Entity Recognition for Medical Dialogue Based on BERT and Adversarial Training

In the medical dialogue named entity recognition, in order to solve the problem that the traditional named entity recognition method is difficult to recognize medical corpus, this paper proposes a BERT-based medical dialogue named entity recognition method BERT-BiLSTM-CRF-ADV. First, we use the BERT pre-training model to obtain word vectors with rich semantic information, then send the word vectors to BiLSTM to extract features, and finally input them into CRF for restriction correction and output. During the training process, we use adversarial training to improve the model performance. This paper applies the proposed method to the medical field to perform named entity recognition on medical dialogue materials. Experimental results show that this method achieves 92.82% of the F1 value in medical dialogue materials, which is greater than the baseline model BERT-CRF.