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Syndrome is an important component in Traditional Chinese Medicine (TCM), and it is also a distinctive concept in TCM compared with Western Medicine (WM). Clearly understand the TCM syndrome help researchers digest TCM regularities and bridge TCM and WM. Syndromes are often used in coarse-grained forms, however fine-grained medical information buried in the coarse-grained TCM syndromes would not be considered. In this paper, we empirically investigate Fine-Grained Syndrome Segmentation (FGSS) in TCM by a distantly supervised method to build a noisy labeled data for training CRFs for FGSS in TCM. The feasibility and effectiveness of the method are demonstrated based on a series of elaborate experiments. The best F1-score can reach 0.9177. To the best of our knowledge, our work is the first to focus on finegrained information extraction in Chinese medical texts.

An Empirical Investigation on Fine-Grained Syndrome Segmentation in TCM by Learning a CRF from a Noisy Labeled Data