Parsimonious Language Models for Information Retrieval

Djoerd Hiemstra, Stephen Robertson, Hugo Zaragoza

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

82 Citations (Scopus)
23 Downloads (Pure)

Abstract

We systematically investigate a new approach to estimating the parameters of language models for information retrieval, called parsimonious language models. Parsimonious language models explicitly address the relation between levels of language models that are typically used for smoothing. As such, they need fewer (non-zero) parameters to describe the data. We apply parsimonious models at three stages of the retrieval process: 1) at indexing time; 2) at search time; 3) at feedback time. Experimental results show that we are able to build models that are significantly smaller than standard models, but that still perform at least as well as the standard approaches.
Original languageUndefined
Title of host publicationProceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Place of PublicationNew York, NY, USA
PublisherACM Press
Pages178-185
Number of pages8
ISBN (Print)1-58113-881-4
DOIs
Publication statusPublished - Jul 2004
Event27th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004 - Sheffield, United Kingdom
Duration: 25 Jul 200429 Jul 2004
Conference number: 27

Publication series

Name
PublisherACM Press

Conference

Conference27th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004
Abbreviated titleSIGIR
CountryUnited Kingdom
CitySheffield
Period25/07/0429/07/04

Keywords

  • IR-58635
  • DB-IR: INFORMATION RETRIEVAL
  • EWI-7256
  • METIS-221519

Cite this

Hiemstra, D., Robertson, S., & Zaragoza, H. (2004). Parsimonious Language Models for Information Retrieval. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 178-185). New York, NY, USA: ACM Press. https://doi.org/10.1145/1008992.1009025