Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: A critical assessment of text mining methods in molecular biology

Open Access Report

Recognition of protein/gene names from text using an ensemble of classifiers

GuoDong Zhou1*, Dan Shen12, Jie Zhang12, Jian Su1 and SoonHeng Tan1

Author affiliations

1 Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore

2 School of Computing, the National Univ. of Singapore, 119610, Singapore

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2005, 6(Suppl 1):S7  doi:10.1186/1471-2105-6-S1-S7

Published: 24 May 2005

Abstract

This paper proposes an ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using a simple majority voting strategy. In addition, we incorporate three post-processing modules, including an abbreviation resolution module, a protein/gene name refinement module and a simple dictionary matching module, into the system to further improve the performance. Evaluation shows that our system achieves the best performance from among 10 systems with a balanced F-measure of 82.58 on the closed evaluation of the BioCreative protein/gene name recognitiontask (Task 1A).