A semi-supervised approach for extracting TCM clinical terms based on feature words

BACKGROUND: A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words. METHODS: The extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and leverage extraction results. RESULTS: Experiment results show that the proposed model improves the extraction of five types of TCM clinical terms, including traditional Chinese medicine, symptoms, patterns, diseases and formulas. The best F1-value of the experiment reaches 78.70% on the test dataset. CONCLUSIONS: This method can reduce the cost of manual labeling and improve the result in the NER research of TCM clinical terms.