Study on Named Entity Recognition in Chinese Literatures on Hypertension treatment

Chinese Medical literature research results are more accurate and representative than other medical texts. This paper studies the extraction method of named entity recognition in Chinese medical literatures on hypertension treatment. This paper proposes a Bi-directional Long Short-Term Memory-Conditional Random Fields (BiLSTM-CRF) model based on Attention mechanism for Chinese named entity recognition. BiLSTM-CRF is used as the model infrastructure while the Attention mechanism is used to learn the dependence of each word on the full text. In addition, the dictionary of literature keywords is built to improve the efficiency of recognition. Compared with the routine BiLSTM-CRF model, the recognition effect of the BILSTM-CRF model based on Attention mechanism was better. The value of Precision, Recall and F1 score were 84.6%, 87.9% and 86.2% respectively. The BiLSTM-CRF model based on Attention mechanism can effectively realize named entity recognition in Chinese medical literatures on hypertension treatment.