This article is part of the supplement: A critical assessment of text mining methods in molecular biology
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Recognition of protein/gene names from text using an ensemble of classifiers
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
Citation and License
BMC Bioinformatics 2005, 6(Suppl 1):S7 doi:10.1186/1471-2105-6-S1-S7
Published: 24 May 2005Abstract
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).
