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A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson

  • Chris Welty
  • J. William Murdock
  • Aditya Kalyanpur
  • James Fan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7650)

Abstract

Questions often explicitly request a particular type of answer. One popular approach to answering natural language questions involves filtering candidate answers based on precompiled lists of instances of common answer types (e.g., countries, animals, foods, etc.). Such a strategy is poorly suited to an open domain in which there is an extremely broad range of types of answers, and the most frequently occurring types cover only a small fraction of all answers. In this paper we present an alternative approach called TyCor, that employs soft filtering of candidates using multiple strategies and sources. We find that TyCor significantly outperforms a single-source, single-strategy hard filtering approach, demonstrating both that multi-source multi-strategy outperforms a single source, single strategy, and that its fault tolerance yields significantly better performance than a hard filter.

Keywords

Type Word Question Answering Answer Type Question Analysis Candidate Answer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chris Welty
    • 1
  • J. William Murdock
    • 1
  • Aditya Kalyanpur
    • 1
  • James Fan
    • 1
  1. 1.IBM ResearchUSA

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