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Query polyrepresentation for ranking retrieval systems without relevance judgments

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  • Miles Efron
  • Megan Winget

Abstract

Ranking information retrieval (IR) systems with respect to their effectiveness is a crucial operation during IR evaluation, as well as during data fusion. This article offers a novel method of approaching the system‐ranking problem, based on the widely studied idea of polyrepresentation. The principle of polyrepresentation suggests that a single information need can be represented by many query articulations–what we call query aspects. By skimming the top k (where k is small) documents retrieved by a single system for multiple query aspects, we collect a set of documents that are likely to be relevant to a given test topic. Labeling these skimmed documents as putatively relevant lets us build pseudorelevance judgments without undue human intervention. We report experiments where using these pseudorelevance judgments delivers a rank ordering of IR systems that correlates highly with rankings based on human relevance judgments.

Suggested Citation

  • Miles Efron & Megan Winget, 2010. "Query polyrepresentation for ranking retrieval systems without relevance judgments," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(6), pages 1081-1091, June.
    • Handle: RePEc:bla:jamist:v:61:y:2010:i:6:p:1081-1091
    DOI: 10.1002/asi.21310
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