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A study of the dirichlet priors for term frequency normalisation

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SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 465 - 471

https://doi.org/10.1145/1076034.1076114

Published: 15 August 2005 Publication History

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Abstract

In Information Retrieval (IR), the Dirichlet Priors have been applied to the smoothing technique of the language modeling approach. In this paper, we apply the Dirichlet Priors to the term frequency normalisation of the classical BM25 probabilistic model and the Divergence from Randomness PL2 model. The contributions of this paper are twofold. First, through extensive experiments on four TREC collections, we show that the newly generated models, to which the Dirichlet Priors normalisation is applied, provide robust and effective performance. Second, we propose a novel theoretically-driven approach to the automatic parameter tuning of the Dirichlet Priors normalisation. Experiments show that this tuning approach optimises the retrieval performance of the newly generated Dirichlet Priors-based weighting models.

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Cited By

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Dang ELuk RAllan J(2021)A Comparison between Term-Independence Retrieval Models for Ad Hoc RetrievalACM Transactions on Information Systems10.1145/348361240:3(1-37)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3483612
Alma’aitah WTalib AOsman M(2021)Towards adaptive structured Dirichlet smoothing model for digital resource objectsMultimedia Tools and Applications10.1007/s11042-020-10305-wOnline publication date: 9-Jan-2021
https://doi.org/10.1007/s11042-020-10305-w
Nandedkar SPatil J(2021)SeAbOM: Semi-supervised Learning for Aspect-Based Opinion MiningProceedings of International Conference on Data Science and Applications10.1007/978-981-16-5120-5_36(479-489)Online publication date: 23-Nov-2021
https://doi.org/10.1007/978-981-16-5120-5_36
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Index Terms

A study of the dirichlet priors for term frequency normalisation
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

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: 15 August 2005

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SIGIR

SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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

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Cited By

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Dang ELuk RAllan J(2021)A Comparison between Term-Independence Retrieval Models for Ad Hoc RetrievalACM Transactions on Information Systems10.1145/348361240:3(1-37)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3483612
Alma’aitah WTalib AOsman M(2021)Towards adaptive structured Dirichlet smoothing model for digital resource objectsMultimedia Tools and Applications10.1007/s11042-020-10305-wOnline publication date: 9-Jan-2021
https://doi.org/10.1007/s11042-020-10305-w
Nandedkar SPatil J(2021)SeAbOM: Semi-supervised Learning for Aspect-Based Opinion MiningProceedings of International Conference on Data Science and Applications10.1007/978-981-16-5120-5_36(479-489)Online publication date: 23-Nov-2021
https://doi.org/10.1007/978-981-16-5120-5_36
Ying ZHuang JZhou J(2019)A New Digital Signal Processing Based Model With Multi-Aspect Term Frequency for Information RetrievalIEEE Access10.1109/ACCESS.2019.29462887(160738-160754)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2946288
Lipani ARoelleke TLupu MHanbury A(2018)A systematic approach to normalization in probabilistic modelsInformation Retrieval Journal10.1007/s10791-018-9334-121:6(565-596)Online publication date: 30-Jun-2018
https://doi.org/10.1007/s10791-018-9334-1
Gupta YSaini A(2017)A novel Fuzzy-PSO term weighting automatic query expansion approach using combined semantic filteringKnowledge-Based Systems10.1016/j.knosys.2017.09.004136:C(97-120)Online publication date: 15-Nov-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.09.004
Yang PFang HCarterette BFang HLalmas MNie J(2016)Estimating Retrieval Performance Bound for Single Term QueriesProceedings of the 2016 ACM International Conference on the Theory of Information Retrieval10.1145/2970398.2970428(237-240)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2970398.2970428
Yang PFang HCarterette BFang HLalmas MNie J(2016)A Reproducibility Study of Information Retrieval ModelsProceedings of the 2016 ACM International Conference on the Theory of Information Retrieval10.1145/2970398.2970415(77-86)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2970398.2970415
Paik J(2016)Parameterized Decay Model for Information RetrievalACM Transactions on Intelligent Systems and Technology10.1145/28007947:3(1-21)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1145/2800794
Lipani ALupu MHanbury AAizawa AAllan JCroft Bde Vries AZhai C(2015)Verboseness Fission for BM25 Document Length NormalizationProceedings of the 2015 International Conference on The Theory of Information Retrieval10.1145/2808194.2809486(385-388)Online publication date: 27-Sep-2015
https://dl.acm.org/doi/10.1145/2808194.2809486
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