Bayesian Extension to the Language Model for Ad Hoc Information Retrieval

H. Zaragoza, Djoerd Hiemstra, M. Tipping, S.E. Robertson

Research output: Contribution to conferencePaperAcademicpeer-review

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.
Original languageUndefined
Pages4-9
Number of pages6
DOIs
Publication statusPublished - Aug 2003
Event26th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003 - Toronto, Canada
Duration: 28 Jul 20031 Aug 2003
Conference number: 26

Conference

Conference26th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003
Abbreviated titleSIGIR
CountryCanada
CityToronto
Period28/07/031/08/03

Keywords

  • DB-IR: INFORMATION RETRIEVAL
  • EWI-7381
  • IR-63543

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