Paper Info
Title
A preliminary work on symptom name recognition from free-text clinical records of traditional chinese medicine using conditional random fields and reasonable features
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.
Year
Venue
Keywords
2012
BioNLP@HLT-NAACL
detailed analysis,conditional random field,symptom name recognition f-measure,recognition error rate,helpful result,symptom name recognition,recognition rate,reasonable feature,preliminary work,free-text clinical record,experiment setting,conditional random fields,symptom name
Field
DocType
Citations 
Conditional random field,Computer science,Word error rate,Traditional Chinese medicine,Natural language processing,Artificial intelligence,CRFS,Machine learning
Conference
7
PageRank 
References 
Authors
0.46
17
5
Name
Order
Citations
PageRank
Yaqiang Wang1373.02
Yiguang Liu233837.15
Zhonghua Yu3474.54
Li Chen4181.40
Yongguang Jiang5423.56