research-article

Incremental blind feedback: An effective approach to automatic query expansion

Authors:

Swapan K. ParuiAuthors Info & Claims

ACM Transactions on Asian Language Information Processing (TALIP), Volume 13, Issue 3

Article No.: 13, Pages 1 - 22

https://doi.org/10.1145/2611521

Published: 03 October 2014 Publication History

Abstract

Automatic query expansion (AQE) is a useful technique for enhancing the effectiveness of information retrieval systems. In this article, we propose a novel AQE algorithm which first adopts a systematic incremental approach to choose feedback documents from the top retrieved set and then selects the expansion terms aggregating the scores from each feedback set. We also devise a term selection measure and a number of weighting schemes based on easily computable features. A set of experiments with a large number of standard test collections reveals that the proposed incremental blind feedback algorithm outperforms a number of state-of-the-art query expansion methods with remarkable significance and consistency.

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Cited By

Gupta VDixit A(2023)Recent Query Reformulation Approaches for Information Retrieval System - A SurveyRecent Advances in Computer Science and Communications10.2174/266625581566622040409192016:1Online publication date: Jan-2023
https://doi.org/10.2174/2666255815666220404091920
Yan CFang XLi Y(2022)UFTDRDH: A novel user-oriented solution for full-text database retrieval in Chinese digital humanitiesJournal of Information Science10.1177/01655515221133529(016555152211335)Online publication date: 22-Nov-2022
https://doi.org/10.1177/01655515221133529
Yang SZheng XYin XMao HZhao D(2020)An Algorithm of Query Expansion for Chinese EMR Retrieval by Improving Expansion Term Weights and Retrieval ScoresIEEE Access10.1109/ACCESS.2020.30330178(200063-200072)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3033017
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Index Terms

Incremental blind feedback: An effective approach to automatic query expansion
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Transactions on Asian Language Information Processing

ACM Transactions on Asian Language Information Processing Volume 13, Issue 3

September 2014

83 pages

ISSN:1530-0226

EISSN:1558-3430

DOI:10.1145/2676410

Editor:
Richard Sproat
Google, Inc., USA

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Copyright © 2014 ACM.

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: 03 October 2014

Accepted: 01 April 2014

Revised: 01 February 2014

Received: 01 November 2013

Published in TALIP Volume 13, Issue 3

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Cited By

Gupta VDixit A(2023)Recent Query Reformulation Approaches for Information Retrieval System - A SurveyRecent Advances in Computer Science and Communications10.2174/266625581566622040409192016:1Online publication date: Jan-2023
https://doi.org/10.2174/2666255815666220404091920
Yan CFang XLi Y(2022)UFTDRDH: A novel user-oriented solution for full-text database retrieval in Chinese digital humanitiesJournal of Information Science10.1177/01655515221133529(016555152211335)Online publication date: 22-Nov-2022
https://doi.org/10.1177/01655515221133529
Yang SZheng XYin XMao HZhao D(2020)An Algorithm of Query Expansion for Chinese EMR Retrieval by Improving Expansion Term Weights and Retrieval ScoresIEEE Access10.1109/ACCESS.2020.30330178(200063-200072)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3033017
Bouchakwa MAyadi YAmous I(2020)An ambiguous tag-based query reformulation technique for an effective semantic-based social image researchProcedia Computer Science10.1016/j.procs.2020.08.053176(508-520)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.08.053
Hu GPeng MZhang YXie QYuan M(2020)Neural joint attention code search over structure embeddings for software Q&A sitesJournal of Systems and Software10.1016/j.jss.2020.110773(110773)Online publication date: Aug-2020
https://doi.org/10.1016/j.jss.2020.110773
Azad HDeepak A(2019)Query expansion techniques for information retrieval: A surveyInformation Processing & Management10.1016/j.ipm.2019.05.00956:5(1698-1735)Online publication date: Oct-2019
https://doi.org/10.1016/j.ipm.2019.05.009
Nasir JVarlamis IIshfaq S(2019)A knowledge-based semantic framework for query expansionInformation Processing & Management10.1016/j.ipm.2019.04.00756:5(1605-1617)Online publication date: Oct-2019
https://doi.org/10.1016/j.ipm.2019.04.007
Hao SShi CNiu ZCao L(2019)Modeling positive and negative feedback for improving document retrievalExpert Systems with Applications10.1016/j.eswa.2018.11.035120(253-261)Online publication date: May-2019
https://doi.org/10.1016/j.eswa.2018.11.035
Nie LJiang HRen ZSun ZLi X(2016)Query Expansion Based on Crowd Knowledge for Code SearchIEEE Transactions on Services Computing10.1109/TSC.2016.25601659:5(771-783)Online publication date: 1-Sep-2016
https://doi.org/10.1109/TSC.2016.2560165

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