Recognition of protein/gene names from text using an ensemble of classifiers.
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).
Zhou
GuoDong
G
Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore. zhougd@i2r.a-star.edu.sg
Shen
Dan
D
Zhang
Jie
J
Su
Jian
J
Tan
SoonHeng
S
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
D011506:Proteins