Recognizing names in biomedical texts using mutual information independence model and SVM plus sigmoid.
In this paper, we present a biomedical name recognition system, called PowerBioNE. In order to deal with the special phenomena in the biomedical domain, various evidential features are proposed and integrated through a mutual information independence model (MIIM). In addition, a support vector machine (SVM) plus sigmoid is proposed to resolve the data sparseness problem in the MIIM. In this way, the data sparseness problem in MIIM-based biomedical name recognition can be resolved effectively and a biomedical name recognition system with better performance and better portability can be achieved. Finally, we present two post-processing modules to deal with the nested entity name and abbreviation phenomena in the biomedical domain to further improve the performance. Evaluation shows that our system achieves F-measures of 69.1 and 71.2 on the 23 classes of GENIA V1.1 and V3.0, respectively. In particular, our system achieves an F-measure of 77.8 on the "protein" class of GENIA V3.0. It also shows that our system outperforms the best-reported system on GENIA V1.1 and V3.0.
Zhou
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Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. zhougd@i2.a-star.edu.sg
D000043:Abstracting and Indexing / Q000379:methods*; D001185:Artificial Intelligence; D001695:Biology; D003628:Database Management Systems; D016206:Databases, Bibliographic; D016247:Information Storage and Retrieval / Q000379:methods; D008511:Medicine; D009323:Natural Language Processing; D010506:Periodicals as Topic; D012660:Semantics; D009626:Terminology as Topic; D018875:Vocabulary, Controlled