Query likelihood with negative query generation

Yuanhua Lv, Chengxiang Zhai

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


The query likelihood retrieval function has proven to be empirically effective for many retrieval tasks. From theoretical perspective, however, the justification of the standard query likelihood retrieval function requires an unrealistic assumption that ignores the generation of a "negative query" from a document. This suggests that it is a potentially non-optimal retrieval function. In this paper, we attempt to improve the query likelihood function by bringing back the negative query generation. We propose an effective approach to estimate the probabilities of negative query generation based on the principle of maximum entropy, and derive a more complete query likelihood retrieval function that also contains the negative query generation component. The proposed approach not only bridges the theoretical gap in the existing query likelihood retrieval function, but also improves retrieval effectiveness significantly with no additional computational cost.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Number of pages5
StatePublished - Dec 19 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series


Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/TerritoryUnited States
CityMaui, HI


  • language model
  • negative query generation
  • principle of maximum entropy
  • probability ranking principle
  • query likelihood

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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