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Exploiting Result Consistency to Select Query Expansions for Spoken Content Retrieval

  • Stevan Rudinac
  • Martha Larson
  • Alan Hanjalic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5993)

Abstract

We propose a technique that predicts both if and how expansion should be applied to individual queries. The prediction is made on the basis of the topical consistency of the top results of the initial results lists returned by the unexpanded query and several query expansion alternatives. We use the coherence score, known to capture the tightness of topical clustering structure, and also propose two simplified coherence indicators. We test our technique in a spoken content retrieval task, with the intention of helping to control the effects of speech recognition errors. Experiments use 46 semantic-theme-based queries defined by VideoCLEF 2009 over the TRECVid 2007 and 2008 video data sets. Our indicators make the best choice roughly 50% of the time. However, since they predict the right query expansion in critical cases, overall MAP improves. The approach is computationally lightweight and requires no training data.

Keywords

Speech-transcript-based video retrieval query expansion query performance prediction list coherence source selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stevan Rudinac
    • 1
  • Martha Larson
    • 1
  • Alan Hanjalic
    • 1
  1. 1.Multimedia Information Retrieval LabDelft University of TechnologyDelftThe Netherlands

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