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Context-Based Technique for Biomedical Term Classification Hisham Al-Mubaid

Summary: Context-Based Technique for Biomedical Term Classification
Hisham Al-Mubaid
University of Houston-Clear Lake, Houston, TX 77058 USA
Abstract--The existing volumes of biomedical texts
available online drive the increasing need for automated
techniques to analyze and extract knowledge from these
information repositories. Recognizing and classifying
biomedical terms in these texts is an important step for
developing efficient techniques for knowledge discovery and
information extraction from the literature. This paper
presents a new technique for biomedical term classification in
biomedical texts. The method is based on combing successful
feature selection techniques (MI, X2
) with machine learning
(SVM) for biomedical term classification. We utilize the
advances in feature selection techniques in IR and use them
to select the key features for term identification and
classification. We evaluated the method using Genia 3.0
corpus with about 3,000 to more than 34,000 biomedical term
instances. The technique is effective, achieving impressive


Source: Al-Mubaid, Hisham - School of Science and Computer Engineering, University of Houston-Clear Lake


Collections: Computer Technologies and Information Sciences