A comparative study of methods for estimating query language models with pseudo feedback

Yuanhua Lv, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We systematically compare five representative state-of-the-art methods for estimating query language models with pseudo feedback in ad hoc information retrieval, including two variants of the relevance language model, two variants of the mixture feedback model, and the divergence minimization estimation method. Our experiment results show that a variant of relevance model and a variant of the mixture model tend to outperform other methods. We further propose several heuristics that are intuitively related to the good retrieval performance of an estimation method, and show that the variations in how these heuristics are implemented in different methods provide a good explanation of many empirical observations.

Original languageEnglish (US)
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages1895-1898
Number of pages4
DOIs
StatePublished - 2009
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: Nov 2 2009Nov 6 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

OtherACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Country/TerritoryChina
CityHong Kong
Period11/2/0911/6/09

Keywords

  • Feedback heuristics
  • Language models
  • Pseudo relevance feedback
  • Query language model

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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