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Probabilistic Relevance Models Based on Document and Query Generation

  • John Lafferty
  • ChengXiang Zhai
Chapter
Part of the The Springer International Series on Information Retrieval book series (INRE, volume 13)

Abstract

We give a unified account of the probabilistic semantics underlying the language modeling approach and the traditional probabilistic model for information retrieval, showing that the two approaches can be viewed as being equivalent probabilistically, since they are based on different factorizations of the same generative relevance model. We also discuss how the two approaches lead to different retrieval frameworks in practice, since they involve component models that are estimated quite differently.

Keywords

Language models relevance models generative models 

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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • John Lafferty
    • 1
  • ChengXiang Zhai
    • 2
  1. 1.School of Computer ScienceCarniegie Mellon UniversityUSA
  2. 2.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignUSA

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