A preliminary work on symptom name recognition from free-text clinical records of traditional chinese medicine using conditional random fields and reasonable features

Authors:
Yaqiang Wang;Yiguang Liu;Zhonghua Yu;Li Chen;Yongguang Jiang
Affiliations:
Sichuan University, Chengdu, Sichuan, China;Sichuan University, Chengdu, Sichuan, China;Sichuan University, Chengdu, Sichuan, China;Sichuan University, Chengdu, Sichuan, China;Chengdu University of TCM, Chengdu, Sichuan, China
Venue:
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Year:
2012

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Abstract

A preliminary work on symptom name recognition from free-text clinical records (FCRs) of traditional Chinese medicine (TCM) is depicted in this paper. This problem is viewed as labeling each character in FCRs of TCM with a pre-defined tag ("B-SYC", "I-SYC" or "O-SYC") to indicate the character's role (a beginning, inside or outside part of a symptom name). The task is handled by Conditional Random Fields (CRFs) based on two types of features. The symptom name recognition F-Measure can reach up to 62.829% with recognition rate 93.403% and recognition error rate 52.665% under our experiment settings. The feasibility and effectiveness of the methods and reasonable features are verified, and several interesting and helpful results are shown. A detailed analysis for recognizing symptom names from FCRs of TCM is presented through analyzing labeling results of CRFs.