R/quest: A Question Answering System

  • Joan Morrissey
  • Ruoxuan Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8132)


In this paper, we discuss our novel, open-domain question answering (Q/A) system, R/quest. We use web page snippets from GoogleTM to extract short paragraphs that become candidate answers. We performed an evaluation that showed, on average, 1.4 times higher recall and a slightly higher precision by using a question expansion method. We have modified the Cosine Coefficient Similarity Measure to take into account the rank position of a candidate answer and its length. This produces an effective ranking scheme. We have a new question refinement method that improves recall. We further enhanced performance by adding a Boolean NOT operator. R/quest on average provides an answer within the top 2 to 3 paragraphs shown to the user. We consider this to be a considerable advance over search engines that provide millions of ranked web pages which must be searched manually to find the information needed.


Question answering systems candidate answers information retrieval web crawling question expansion question refinement modified cosine similarity measure system evaluation 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Joan Morrissey
    • 1
  • Ruoxuan Zhao
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

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