Article

A study of Poisson query generation model for information retrieval

Authors:

ChengXiang ZhaiAuthors Info & Claims

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 319 - 326

https://doi.org/10.1145/1277741.1277797

Published: 23 July 2007 Publication History

Abstract

Many variants of language models have been proposed for information retrieval. Most existing models are based on multinomial distribution and would score documents based on query likelihood computed based on a query generation probabilistic model. In this paper, we propose and study a new family of query generation models based on Poisson distribution. We show that while in their simplest forms, the new family of models and the existing multinomial models are equivalent. However, based on different smoothing methods, the two families of models behave differently. We show that the Poisson model has several advantages, including naturally accommodating per-term smoothing and modeling accurate background more efficiently. We present several variants of the new model corresponding to different smoothing methods, and evaluate them on four representative TREC test collections. The results show that while their basic models perform comparably, the Poisson model can out perform multinomial model with per-term smoothing. The performance can be further improved with two-stage smoothing.

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Cited By

Poux-Médard GVelcin JLoudcher S(2023)Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion NetworksAdvances in Information Retrieval10.1007/978-3-031-28238-6_47(562-570)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28238-6_47
Wang XWei XGao SLiu YLi Z(2019)A Novel Auction-Based Query Pricing SchemaInternational Journal of Parallel Programming10.1007/s10766-017-0534-x47:4(759-780)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10766-017-0534-x
Kolomvatsos KAnagnostopoulos C(2019)A probabilistic model for assigning queries at the edgeComputing10.1007/s00607-019-00767-8102:4(865-892)Online publication date: 18-Nov-2019
https://doi.org/10.1007/s00607-019-00767-8
Show More Cited By

Index Terms

A study of Poisson query generation model for information retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

July 2007

946 pages

ISBN:9781595935977

DOI:10.1145/1277741

General Chairs:
Wessel Kraaij
TNO, The Netherlands
,
Arjen P. de Vries
CWI, The Netherlands
,
Program Chairs:
Charles L. A. Clarke
University of Waterloo, Canada
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Noriko Kando
National Institute of Informatics, Japan

Copyright © 2007 ACM.

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Publication History

Published: 23 July 2007

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SIGIR07

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SIGIR07: The 30th Annual International SIGIR Conference

July 23 - 27, 2007

Amsterdam, The Netherlands

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Poux-Médard GVelcin JLoudcher S(2023)Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion NetworksAdvances in Information Retrieval10.1007/978-3-031-28238-6_47(562-570)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28238-6_47
Wang XWei XGao SLiu YLi Z(2019)A Novel Auction-Based Query Pricing SchemaInternational Journal of Parallel Programming10.1007/s10766-017-0534-x47:4(759-780)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10766-017-0534-x
Kolomvatsos KAnagnostopoulos C(2019)A probabilistic model for assigning queries at the edgeComputing10.1007/s00607-019-00767-8102:4(865-892)Online publication date: 18-Nov-2019
https://doi.org/10.1007/s00607-019-00767-8
Mehrotra RYilmaz EKando NSakai TJoho HLi Hde Vries AWhite R(2017)Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric ApproachProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080823(285-294)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080823
Barbieri NManco GRitacco E(2017)Survival Factorization on Diffusion NetworksMachine Learning and Knowledge Discovery in Databases10.1007/978-3-319-71249-9_41(684-700)Online publication date: 30-Dec-2017
https://doi.org/10.1007/978-3-319-71249-9_41
Zhao MOhshima HTanaka K(2016)Paraphrasing Sentential Queries by Incorporating Coordinate RelationshipJournal of Information Processing10.2197/ipsjjip.24.72124:4(721-731)Online publication date: 2016
https://doi.org/10.2197/ipsjjip.24.721
Zhao MOhshima HTanaka KAnderst-Kotsis GIndrawan-Santiago M(2015)Sentential query rewriting via mutual reinforcement of paraphrase-coordinate relationshipsProceedings of the 17th International Conference on Information Integration and Web-based Applications & Services10.1145/2837185.2837222(1-10)Online publication date: 11-Dec-2015
https://dl.acm.org/doi/10.1145/2837185.2837222
Lv YZhai C(2015)Negative query generation: bridging the gap between query likelihood retrieval models and relevanceInformation Retrieval Journal10.1007/s10791-015-9257-z18:4(359-378)Online publication date: 6-Jun-2015
https://doi.org/10.1007/s10791-015-9257-z
Paik JOard DLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)A Fixed-Point Method for Weighting Terms in Verbose Informational QueriesProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661957(131-140)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2661957
Bakhtiari ABouguila N(2014)A variational Bayes model for count data learning and classificationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2014.06.02335(176-186)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.engappai.2014.06.023
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