TY - GEN
T1 - Term recognition using conditional random fields
AU - Zhang, Xing
AU - Song, Yan
AU - Fang, Alex Chengyu
PY - 2010
Y1 - 2010
N2 - A machine learning framework, Conditional Random fields (CRF), is constructed in this study, which exploits syntactic information to recognize biomedical terms. Features used in this CRF framework focus on syntactic information in different levels, including parent nodes, syntactic functions, syntactic paths and term ratios. A series of experiments have been done to study the effects of training sizes, general term recognition and novel term recognition. The experiment results show that features as syntactic paths and term ratios can achieve good precision of term recognition, including both general terms and novel terms. However, the recall of novel term recognition is still unsatisfactory, which calls for more effective features to be used. All in all, as this research studies in depth the uses of some unique syntactic features, it is innovative in respect of constructing machine learning based term recognition system.
AB - A machine learning framework, Conditional Random fields (CRF), is constructed in this study, which exploits syntactic information to recognize biomedical terms. Features used in this CRF framework focus on syntactic information in different levels, including parent nodes, syntactic functions, syntactic paths and term ratios. A series of experiments have been done to study the effects of training sizes, general term recognition and novel term recognition. The experiment results show that features as syntactic paths and term ratios can achieve good precision of term recognition, including both general terms and novel terms. However, the recall of novel term recognition is still unsatisfactory, which calls for more effective features to be used. All in all, as this research studies in depth the uses of some unique syntactic features, it is innovative in respect of constructing machine learning based term recognition system.
KW - Conditional random fields
KW - General term
KW - Novel term
KW - Syntactic function
KW - Term recognition
KW - Tracking
UR - https://www.scopus.com/pages/publications/78649288876
U2 - 10.1109/NLPKE.2010.5587809
DO - 10.1109/NLPKE.2010.5587809
M3 - Conference contribution
AN - SCOPUS:78649288876
SN - 9781424468966
T3 - Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2010
BT - Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE, 2010
T2 - 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2010
Y2 - 21 August 2010 through 23 August 2010
ER -