title: Recognition of protein/gene names from text using an ensemble of classifiers.
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 recognition task (Task 1A).
authors.affiliation: Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore. zhougd@i2r.a-star.edu.sg
authors.forename: GuoDong
authors.initials: G
authors.lastname: Zhou
authors.forename: Dan
authors.initials: D
authors.lastname: Shen
authors.forename: Jie
authors.initials: J
authors.lastname: Zhang
authors.forename: Jian
authors.initials: J
authors.lastname: Su
authors.forename: SoonHeng
authors.initials: S
authors.lastname: Tan
mesh_terms: D035843:Biomedical Research / Q000145:classification / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D005796:Genes; D008390:Markov Chains; D008513:Medicine in Literature; D011506:Proteins / Q000145:classification* / Q000235:genetics*; D011897:Random Allocation; D021641:Recognition, Psychology; D009626:Terminology as Topic
chemical_list: D011506:Proteins