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A Topic Model Scoring Approach for Personalized QA Systems

  • Hamidreza Chinaei
  • Luc Lamontagne
  • François Laviolette
  • Richard Khoury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8655)

Abstract

To support the personalization of Question Answering (QA) systems, we propose a new probabilistic scoring approach based on the topics of the question and candidate answers. First, a set of topics of interest to the user is learned based on a topic modeling approach such as Latent Dirichlet Allocation. Then, the similarity of questions asked by the user to the candidate answers, returned by the search engine, is estimated by calculating the probability of the candidate answer given the question. This similarity is used to re-rank the answers returned by the search engine. Our preliminary experiments show that the reranking highly increases the performance of the QA system estimated based on accuracy and MRR (mean reciprocal rank).

Keywords

Personalized QA User Modeling Topic Modeling 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hamidreza Chinaei
    • 1
  • Luc Lamontagne
    • 1
  • François Laviolette
    • 1
  • Richard Khoury
    • 2
  1. 1.Department of Computer Science and Software EngineeringUniversité LavalCanada
  2. 2.Department of Software EngineeringLakehead UniversityThunder BayCanada

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