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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Bhowan:2015:EuroGP,
  author =       "Urvesh Bhowan and D. J. McCloskey",
  title =        "Genetic Programming for Feature Selection and
                 Question-Answer Ranking in {IBM Watson}",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "153--166",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, IBM Watson,
                 Question answer ranking, Feature selection: Poster",
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1_13",
  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 automatic feature selection
                 mechanisms to identify Watson 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.",
  notes =        "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and
                 EvoApplications2015",
}

Genetic Programming entries for Urvesh Bhowan D J McCloskey

Citations