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A study of query length

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SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 811 - 812

https://doi.org/10.1145/1390334.1390517

Published: 20 July 2008 Publication History

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Abstract

We analyse query length, and fit power-law and Poisson distributions to four different query sets. We provide a practical model for query length, based on the truncation of a Poisson distribution for short queries and a power-law distribution for longer queries, that better fits real query length distributions than earlier proposals.

References

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Cited By

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Ma XGuo JZhang RFan YJi XCheng XLewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)PROP: Pre-training with Representative Words Prediction for Ad-hoc RetrievalProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441777(283-291)Online publication date: 8-Mar-2021
https://dl.acm.org/doi/10.1145/3437963.3441777
Ma XGuo JZhang RFan YLi YCheng XDiaz FShah CSuel TCastells PJones RSakai T(2021)B-PROPProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462869(1513-1522)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462869
Wagaman ADobrow R(2021)ReferencesProbability10.1002/9781119692430.ref(511-514)Online publication date: 11-Jun-2021
https://doi.org/10.1002/9781119692430.ref
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Index Terms

A study of query length
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Digital libraries and archives

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Published In

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

July 2008

934 pages

ISBN:9781605581644

DOI:10.1145/1390334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Mun-Kew Leong
National Library Board, Singapore
,
Program Chairs:
Syung Hyon Myaeng
Information and Communications University, Korea
,
Douglas W. Oard
University of Maryland, College Park, USA
,
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2008

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SIGIR '08

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SIGIR '08: The 31st Annual International ACM SIGIR Conference

July 20 - 24, 2008

Singapore, Singapore

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Ma XGuo JZhang RFan YJi XCheng XLewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)PROP: Pre-training with Representative Words Prediction for Ad-hoc RetrievalProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441777(283-291)Online publication date: 8-Mar-2021
https://dl.acm.org/doi/10.1145/3437963.3441777
Ma XGuo JZhang RFan YLi YCheng XDiaz FShah CSuel TCastells PJones RSakai T(2021)B-PROPProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462869(1513-1522)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462869
Wagaman ADobrow R(2021)ReferencesProbability10.1002/9781119692430.ref(511-514)Online publication date: 11-Jun-2021
https://doi.org/10.1002/9781119692430.ref
Le TJeong HGilbert SChukharev-Hudilainend E(2020)Generating partial civil information model views using a semantic information retrieval approachJournal of Information Technology in Construction10.36680/j.itcon.2020.00225(41-54)Online publication date: 29-Jan-2020
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Mustafaraj ELurie EDevine CHildebrandt MCastillo CCelis ERuggieri STaylor LZanfir-Fortuna G(2020)The case for voter-centered audits of search engines during political electionsProceedings of the 2020 Conference on Fairness, Accountability, and Transparency10.1145/3351095.3372835(559-569)Online publication date: 27-Jan-2020
https://dl.acm.org/doi/10.1145/3351095.3372835
Yu SJin TShi ZLi JPan J(2019)A User Effort Measurement for Query SelectionInformation Retrieval10.1007/978-3-030-31624-2_2(16-25)Online publication date: 18-Sep-2019
https://doi.org/10.1007/978-3-030-31624-2_2
Deveaud RMothe JUllah MNie J(2018)Learning to Adaptively Rank Document Retrieval System ConfigurationsACM Transactions on Information Systems10.1145/323193737:1(1-41)Online publication date: 30-Oct-2018
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Ghosh RKomma SSimmhan YEl-Araby EEl-Ghazawi TPanda D(2018)Adaptive energy-aware scheduling of dynamic event analytics across edge and cloud resourcesProceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing10.1109/CCGRID.2018.00022(72-82)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1109/CCGRID.2018.00022
Hagen MStein B(2018)Weblog AnalysisEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_129(3387-3395)Online publication date: 12-Jun-2018
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Goslin KHofmann M(2017)A Comparison of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia as a Source of A Priori KnowledgeProceedings of the 9th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3158354.3158356(6-13)Online publication date: 8-Dec-2017
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