A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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Modern Information Technologies and Web-based services are faced with the problem of selecting, filtering and managing growing amounts of textual information to which access is usually critical. On one hand, Text Categorization models allow users to browse more easily the set of texts of their own interests, by navigating in category hierarchies. On the other hand, Question/Answering is a method of retrieving information from vast document collections. Inspite of their shared goal, these two information retrieval techniques have been ever applied separately.In this paper we present a Question/Answering system that takes advantage from category information by exploiting several models of question and answer categorization. Knowing the question category has the potential of enhancing a more efficient answer extraction mechanism as the matching of the question category with the answer category allows to (1) re-rank the answers; and (2) eliminate incorrect answers. Experimental results show the effects of question and answer categorization on the overall Question Answeringperformance.