Bayesian extension to the language model for ad hoc information retrieval

  • Authors:
  • Hugo Zaragoza;Djoerd Hiemstra;Michael Tipping

  • Affiliations:
  • Microsoft Research, Cambridge, U.K.;University of Twente, The Netherlands;Microsoft Research, Cambridge, U.K.

  • Venue:
  • Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
  • Year:
  • 2003

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Abstract

We propose a Bayesian extension to the ad-hoc Language Model. Many smoothed estimators used for the multinomial query model in ad-hoc Language Models (including Laplace and Bayes-smoothing) are approximations to the Bayesian predictive distribution. In this paper we derive the full predictive distribution in a form amenable to implementation by classical IR models, and then compare it to other currently used estimators. In our experiments the proposed model outperforms Bayes-smoothing, and its combination with linear interpolation smoothing outperforms all other estimators.