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A Comparative Analysis of Human and Automatic Query Variants

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J. Shane CulpepperAuthors Info & Claims

ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval

Pages 47 - 50

https://doi.org/10.1145/3341981.3344223

Published: 26 September 2019 Publication History

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Abstract

We present an in-depth comparative analysis of the effectiveness distributions of sets of human-created and automatically-created query variations used to represent the same information need. The automatic variations are generated using Bing's click graph. Experiments performed with TREC datasets show that using automatic variations for retrieval can result in similar effectiveness to that of using human variations, although the two types of variations can be appreciably different in several important respects --- e.g., their similarities and corresponding retrieved lists.

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Cited By

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Zerhoudi SGranitzer M(2024)Generative Agents Navigating Digital LibrariesSustainability and Empowerment in the Context of Digital Libraries10.1007/978-981-96-0865-2_14(171-188)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0865-2_14
Wang SScells HKoopman BPotthast MZuccon G(2023)Generating Natural Language Queries for More Effective Systematic Review Screening PrioritisationProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625322(73-83)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625322
Mackie IOwoicho PGemmell CFischer SMacAvaney SDalton JAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)CODEC: Complex Document and Entity CollectionProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531712(3067-3077)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531712
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Index Terms

A Comparative Analysis of Human and Automatic Query Variants
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking
      1. Combination, fusion and federated search
      2. Language models

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Published In

ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval

September 2019

273 pages

ISBN:9781450368810

DOI:10.1145/3341981

General Chairs:
Yi Fang
Santa Clara University, USA
,
Yi Zhang
University of California, Santa Cruz, USA
,
Program Chairs:
James Allan
University of Massachusetts, Amherst, USA
,
Krisztian Balog
University of Stavanger, Norway
,
Ben Carterette
Spotify, USA
,
Jiafeng Guo
Chinese Academy of Sciences, China

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 the author(s) 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: 26 September 2019

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ICTIR '19: The 2019 ACM SIGIR International Conference on the Theory of Information Retrieval

October 2 - 5, 2019

CA, Santa Clara, USA

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ICTIR '19 Paper Acceptance Rate 20 of 41 submissions, 49%;

Overall Acceptance Rate 235 of 527 submissions, 45%

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Zerhoudi SGranitzer M(2024)Generative Agents Navigating Digital LibrariesSustainability and Empowerment in the Context of Digital Libraries10.1007/978-981-96-0865-2_14(171-188)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0865-2_14
Wang SScells HKoopman BPotthast MZuccon G(2023)Generating Natural Language Queries for More Effective Systematic Review Screening PrioritisationProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625322(73-83)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625322
Mackie IOwoicho PGemmell CFischer SMacAvaney SDalton JAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)CODEC: Complex Document and Entity CollectionProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531712(3067-3077)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531712
Alaofi MGallagher LMckay DSaling LSanderson MScholer FSpina DWhite RAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Where Do Queries Come From?Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531711(2850-2862)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531711
Breuer TFuhr NSchaer P(2022)Validating Simulations of User Query VariantsAdvances in Information Retrieval10.1007/978-3-030-99736-6_6(80-94)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99736-6_6
Di Nunzio GFaggioli G(2021)A Study of a Gain Based Approach for Query Aspects in Recall Oriented TasksApplied Sciences10.3390/app1119907511:19(9075)Online publication date: 29-Sep-2021
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Mackenzie JMoffat AHasibi FFang YAizawa A(2021)Modality Effects When Simulating User Querying TasksProceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3471158.3472244(197-201)Online publication date: 11-Jul-2021
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Benham RMackenzie JCulpepper JMoffat AScholer FThomas PElsweiler DJoho HKando NSmith C(2021)Different Keystrokes for Different FolksProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446054(331-335)Online publication date: 14-Mar-2021
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Liu BLu XCulpepper J(2021)Strong natural language query generationInformation Retrieval Journal10.1007/s10791-021-09395-3Online publication date: 15-Jul-2021
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Evaluation of phrasal query suggestions

Exploiting underrepresented query aspects for automatic query expansion

Diversifying Query Auto-Completion

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