Biomedical term recognition with the perceptron HMM algorithm

Authors:
Sittichai Jiampojamarn;Grzegorz Kondrak;Colin Cherry
Affiliations:
University of Alberta, Edmonton, Canada;University of Alberta, Edmonton, Canada;University of Alberta, Edmonton, Canada
Venue:
LNLBioNLP '06 Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology
Year:
2006

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Abstract

We propose a novel approach to the identification of biomedical terms in research publications using the Perceptron HMM algorithm. Each important term is identified and classified into a biomedical concept class. Our proposed system achieves a 68.6% F-measure based on 2,000 training Medline abstracts and 404 unseen testing Medline abstracts. The system achieves performance that is close to the state-of-the-art using only a small feature set. The Perceptron HMM algorithm provides an easy way to incorporate many potentially interdependent features.