Named Entity Recognition Using Support Vector Machine for Filipino Text Documents

Abstract

Named entity recognition involves processing of texts to identify and classify entities such as names of person, place, organization, etc. In this study, a system for a named entity recognizer for Filipino texts using support vector machine was developed, and its performance was evaluated and compared to an existing named entity recognizer intended for the same language, but uses a rule-based approach. Based from the results, the named entity recognizer using support vector machine performed best in tagging named entity class date with 95.52% f-measure, achieving 84.97% overall f-measure.