A semi-supervised approach for extracting TCM clinical terms based on feature words.
BMC medical informatics and decision making(2020)
摘要
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.
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