A Trainable Multi-factored QA System

  • Radu Ion
  • Dan Ştefănescu
  • Alexandru Ceauşu
  • Dan Tufiş
  • Elena Irimia
  • Verginica Barbu Mititelu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6241)

Abstract

This paper reports on the construction and testing of a new Question Answering (QA) system, implemented as an workflow which builds on several web services developed at the Research Institute for Artificial Intelligence (RACAI).The evaluation of the system has been independently done by the organizers of the Romanian-Romanian task of the ResPubliQA 2009 exercise and has been rated the best performing system with the highest improvement due to the NLP technology over a baseline state-of-the-art IR system. We describe a principled way of combining different relevance measures for obtaining a general relevance (to the user’s question) score that will serve as the sort key for the returned paragraphs. The system was trained on a specific corpus, but its functionality is independent on the linguistic register of the training data. The trained QA system that participated in the ResPubliQA shared task is available as a web application at http://www2.racai.ro/sir-resdec/.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Radu Ion
    • 1
  • Dan Ştefănescu
    • 1
  • Alexandru Ceauşu
    • 1
  • Dan Tufiş
    • 1
  • Elena Irimia
    • 1
  • Verginica Barbu Mititelu
    • 1
  1. 1.Research Institute for Artificial IntelligenceRomanian AcademyBucharestRomania

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