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

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Abstract

A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words.
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.
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.
This method can reduce the cost of manual labeling and improve the result in the NER research of TCM clinical terms.