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Genetic Programming for Feature Selection and Question-Answer Ranking in IBM Watson

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Genetic Programming (EuroGP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9025))

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Abstract

IBM Watson is an intelligent open-domain question answering system capable of finding correct answers to natural language questions in real-time. Watson uses machine learning over a large heterogeneous feature set derived from many distinct natural language processing algorithms to identify correct answers. This paper develops a Genetic Programming (GP) approach for feature selection in Watson by evolving ranking functions to order candidate answers generated in Watson. We leverage GP’s automatic feature selection mechanisms to identify Watson’s key features through the learning process. Our experiments show that GP can evolve relatively simple ranking functions that use much fewer features from the original Watson feature set to achieve comparable performances to Watson. This methodology can aid Watson implementers to better identify key components in an otherwise large and complex system for development, troubleshooting, and/or customer or domain-specific enhancements.

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Notes

  1. 1.

    The ground-truth dictionary is manually created and curated by the Watson development team.

  2. 2.

    Obtained from: http://dumps.wikimedia.org/.

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Acknowledgements

We would like to thank IBM Research Staff members Dr. Vittorio Castelli and Dr J. William Murdock for their valuable contributions to this paper.

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Authors and Affiliations

  1. IBM Ireland (IBM Technology Campus), Dublin, Ireland

    Urvesh Bhowan & D. J. McCloskey

Authors
  1. Urvesh Bhowan
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  2. D. J. McCloskey
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Corresponding author

Correspondence to Urvesh Bhowan .

Editor information

Editors and Affiliations

  1. Universidade de Coimbra, Coimbra, Portugal

    Penousal Machado

  2. Dalhousie University, Halifax, Nova Scotia, Canada

    Malcolm I. Heywood

  3. University College Dublin, Dublin, Ireland

    James McDermott

  4. Universidade Nova de Lisboa, Lisboa, Portugal

    Mauro Castelli

  5. Universidad de Granada, Granada, Spain

    Pablo García-Sánchez

  6. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

  7. IT University of Copenhagen, Copenhagen, Denmark

    Sebastian Risi

  8. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Bhowan, U., McCloskey, D.J. (2015). Genetic Programming for Feature Selection and Question-Answer Ranking in IBM Watson. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-16501-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16500-4

  • Online ISBN: 978-3-319-16501-1

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