Effective adaptation of a Hidden Markov Model-based named entity recognizer for biomedical domain

Authors:
Dan Shen;Jie Zhang;Guodong Zhou;Jian Su;Chew-Lim Tan
Affiliations:
Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;National University of Singapore, Singapore
Venue:
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Year:
2003

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Abstract

In this paper, we explore how to adapt a general Hidden Markov Model-based named entity recognizer effectively to biomedical domain. We integrate various features, including simple deterministic features, morphological features, POS features and semantic trigger features, to capture various evidences especially for biomedical named entity and evaluate their contributions. We also present a simple algorithm to solve the abbreviation problem and a rule-based method to deal with the cascaded phenomena in biomedical domain. Our experiments on GENIA V3.0 and GENIA V1.1 achieve the 66.1 and 62.5 F-measure respectively, which outperform the previous best published results by 8.1 F-measure when using the same training and testing data.