Context-Based Technique for Biomedical Term Classification

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 accuracy, precision, and recall; and with F-score approaching -90%, the method is superior or very competitive with the recently published results.