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A comparative study of methods for estimating query language models with pseudo feedback

Authors:

Yuanhua Lv,

ChengXiang ZhaiAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1895 - 1898

https://doi.org/10.1145/1645953.1646259

Published: 02 November 2009 Publication History

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Abstract

We systematically compare five representative state-of-the-art methods for estimating query language models with pseudo feedback in ad hoc information retrieval, including two variants of the relevance language model, two variants of the mixture feedback model, and the divergence minimization estimation method. Our experiment results show that a variant of relevance model and a variant of the mixture model tend to outperform other methods. We further propose several heuristics that are intuitively related to the good retrieval performance of an estimation method, and show that the variations in how these heuristics are implemented in different methods provide a good explanation of many empirical observations.

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Cited By

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Pan MZhou SLi TLiu YPei QHuang AHuang J(2024)Utilizing passage‐level relevance and kernel pooling for enhancing BERT‐based document rerankingComputational Intelligence10.1111/coin.1265640:3Online publication date: 7-Jun-2024
https://doi.org/10.1111/coin.12656
Chen FFang HYoshioka MKiseleva JAliannejadi M(2023)Learn to be Fair without Labels: A Distribution-based Learning Framework for Fair RankingProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605132(23-32)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605132
Li HMourad AZhuang SKoopman BZuccon G(2023)Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and PitfallsACM Transactions on Information Systems10.1145/357072441:3(1-40)Online publication date: 10-Apr-2023
https://dl.acm.org/doi/10.1145/3570724
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Index Terms

A comparative study of methods for estimating query language models with pseudo feedback
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

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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Published: 02 November 2009

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Pan MZhou SLi TLiu YPei QHuang AHuang J(2024)Utilizing passage‐level relevance and kernel pooling for enhancing BERT‐based document rerankingComputational Intelligence10.1111/coin.1265640:3Online publication date: 7-Jun-2024
https://doi.org/10.1111/coin.12656
Chen FFang HYoshioka MKiseleva JAliannejadi M(2023)Learn to be Fair without Labels: A Distribution-based Learning Framework for Fair RankingProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605132(23-32)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605132
Li HMourad AZhuang SKoopman BZuccon G(2023)Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and PitfallsACM Transactions on Information Systems10.1145/357072441:3(1-40)Online publication date: 10-Apr-2023
https://dl.acm.org/doi/10.1145/3570724
Wen XChen XChen XHe BSun LChen HDuh WHuang HKato MMothe JPoblete B(2023)Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592028(2209-2214)Online publication date: 19-Jul-2023
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Hambarde KProença H(2023)Information Retrieval: Recent Advances and BeyondIEEE Access10.1109/ACCESS.2023.329577611(76581-76604)Online publication date: 2023
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Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls

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