An increased interest has been noticed in Named Entity Recognition, particularly in the field of biomedical domain as identifying and extracting biomedical entities such as genes, diseases, proteins and drugs are tedious and demanding due to its ambiguity in biomedical terms. Named entity Recognition task consists of two phases. The first phase recognizes and extracts the entities whereas the second phase classifies the extracted entities under its associated classes. This research work is focused on the second phase of NER that is classifying the extracted entities with its associated class. In order to classify the entities, Relevance Vector Machine is trained and tested on two different datasets. For comparison purpose, HMM and SVM methods have been applied on the same datasets. The evaluation results shows that the RVM classifier performs better than HMM and SVM with high accuracy and less period of execution time.

Named Entity Recognition, Biomedical domain , Biomedical entities, Entities Classification, RVM

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