Question answering from the web using knowledge annotation and knowledge mining techniques

  • Authors:
  • Jimmy Lin;Boris Katz

  • Affiliations:
  • MIT Computer Science and Artificial Intelligence Laboratory;MIT Computer Science and Artificial Intelligence Laboratory

  • Venue:
  • CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
  • Year:
  • 2003

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Abstract

We present a strategy for answering fact-based natural language questions that is guided by a characterization of real-world user queries. Our approach, implemented in a system called Aranea, extracts answers from the Web using two different techniques: knowledge annotation and knowledge mining. Knowledge annotation is an approach to answering large classes of frequently occurring questions by utilizing semi\-structured and structured Web sources. Knowledge mining is a statistical approach that leverages massive amounts of Web data to overcome many natural language processing challenges. We have integrated these two different paradigms into a question answering system capable of providing users with concise answers that directly address their information needs.