Answer Filtering via Text Categorization in Question Answering Systems

Research output: Contribution to journalConference article

7 Citations (Scopus)


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. In spite 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 Answering performance.

Original languageEnglish
Pages (from-to)241-248
Number of pages8
JournalProceedings of the International Conference on Tools with Artificial Intelligence
Publication statusPublished - 16 Dec 2003
EventProceedings: 15th IEEE International Conference on Tools with artificial Intelligence - Sacramento, CA, United States
Duration: 3 Nov 20035 Nov 2003


ASJC Scopus subject areas

  • Software

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