Article

Comparing cross-language query expansion techniques by degrading translation resources

Authors:

James MayfieldAuthors Info & Claims

SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 159 - 166

https://doi.org/10.1145/564376.564406

Published: 11 August 2002 Publication History

Abstract

The quality of translation resources is arguably the most important factor affecting the performance of a cross-language information retrieval system. While many investigations have explored the use of query expansion techniques to combat errors induced by translation, no study has yet examined the effectiveness of these techniques across resources of varying quality. This paper presents results using parallel corpora and bilingual wordlists that have been deliberately degraded prior to query translation. Across different languages, translingual resources, and degrees of resource degradation, pre-translation query expansion is tremendously effective. In several instances, pre-translation expansion results in better performance when no translations are available, than when an uncompromised resource is used without pre-translation expansion. We also demonstrate that post-translation expansion using relevance feedback can confer modest performance gains. Measuring the efficacy of these techniques with resources of different quality suggests an explanation for the conflicting reports that have appeared in the literature.

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Cited By

Yang ELawrie DMayfield JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Distillation for Multilingual Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657955(2368-2373)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657955
Chandra GDwivedi S(2024)Query Expansion Using Proposed Location-Based Algorithm for Hindi–English CLIR: Analyzing Three Test CollectionsInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142459001838:05Online publication date: 11-May-2024
https://doi.org/10.1142/S0218001424590018
Nair SYang ELawrie DDuh KMcNamee PMurray KMayfield JOard D(2022)Transfer Learning Approaches for Building Cross-Language Dense Retrieval ModelsAdvances in Information Retrieval10.1007/978-3-030-99736-6_26(382-396)Online publication date: 5-Apr-2022
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Index Terms

Comparing cross-language query expansion techniques by degrading translation resources
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Information retrieval query processing

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Published In

cover image ACM Conferences

SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval

August 2002

478 pages

ISBN:1581135610

DOI:10.1145/564376

General Chair:
Kalervo Järvelin
University of Tampere, Finland
,
Program Chairs:
Micheline Beaulieu
University of Sheffield, UK
,
Ricardo Baeza-Yates
University of Chile, Chile
,
Sung Hyon Myaeng
Chungnam National University, Korea

Copyright © 2002 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 11 August 2002

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SIGIR02: 25th ACM/SIGIR International Conference on Research and Development in Information Retrieval

August 11 - 15, 2002

Tampere, Finland

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SIGIR '02 Paper Acceptance Rate 44 of 219 submissions, 20%;

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

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Cited By

Yang ELawrie DMayfield JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Distillation for Multilingual Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657955(2368-2373)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657955
Chandra GDwivedi S(2024)Query Expansion Using Proposed Location-Based Algorithm for Hindi–English CLIR: Analyzing Three Test CollectionsInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142459001838:05Online publication date: 11-May-2024
https://doi.org/10.1142/S0218001424590018
Nair SYang ELawrie DDuh KMcNamee PMurray KMayfield JOard D(2022)Transfer Learning Approaches for Building Cross-Language Dense Retrieval ModelsAdvances in Information Retrieval10.1007/978-3-030-99736-6_26(382-396)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99736-6_26
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