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Do Easy Topics Predict Effectiveness Better Than Difficult Topics?

  • Kevin Roitero
  • Eddy Maddalena
  • Stefano MizzaroEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

After a network-based analysis of TREC results, Mizzaro and Robertson [4] found the rather unpleasant result that topic ease (i.e., the average effectiveness of the participating systems, measured with average precision) correlates with the ability of topics to predict system effectiveness (defined as topic hubness). We address this issue by: (i) performing a more detailed analysis, and (ii) using three different datasets. Our results are threefold. First, we confirm that the original result is indeed correct and general across datasets. Second, we show that, however, that result is less worrying than what might seem at first glance, since it depends on considering the least effective systems in the analysis. In other terms, easy topics discriminate most and least effective systems, but when focussing on the most effective systems only this is no longer true. Third, we also clarify what happens when using the GMAP metric.

References

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kevin Roitero
    • 1
  • Eddy Maddalena
    • 1
  • Stefano Mizzaro
    • 1
    Email author
  1. 1.Department of Mathematics, Computer Science, and PhysicsUniversity of UdineUdineItaly

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