Article

Combining bidirectional translation and synonymy for cross-language information retrieval

Authors:

Jianqiang Wang,

Douglas W. OardAuthors Info & Claims

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 202 - 209

https://doi.org/10.1145/1148170.1148208

Published: 06 August 2006 Publication History

Abstract

This paper introduces a general framework for the use of translation probabilities in cross-language information retrieval based on the notion that information retrieval fundamentally requires matching what the searcher means with what the author of a document meant. That perspective yields a computational formulation that provides a natural way of combining what have been known as query and document translation. Two well-recognized techniques are shown to be a special case of this model under restrictive assumptions. Cross-language search results are reported that are statistically indistinguishable from strong monolingual baselines for both French and Chinese documents.

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Pasupa KSeneewong Na Ayutthaya T(2021)Hybrid Deep Learning Models for Thai Sentiment AnalysisCognitive Computation10.1007/s12559-020-09770-014:1(167-193)Online publication date: 4-Mar-2021
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Elayeb BBounhas I(2016)Arabic Cross-Language Information RetrievalACM Transactions on Asian and Low-Resource Language Information Processing10.1145/278921015:3(1-44)Online publication date: 28-Jan-2016
https://dl.acm.org/doi/10.1145/2789210
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Index Terms

Combining bidirectional translation and synonymy for cross-language information retrieval
1. Information systems
  1. Information retrieval

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Information & Contributors

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Published In

cover image ACM Conferences

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

August 2006

768 pages

ISBN:1595933697

DOI:10.1145/1148170

General Chair:
Efthimis N. Efthimiadis
University of Washington
,
Program Chairs:
Susan Dumais
Microsoft Research, Redmond
,
David Hawking
CSIRO ICT Centre, Canberra, Australia
,
Kalervo Järvelin,
University of Tampere, Finland

Copyright © 2006 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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Published: 06 August 2006

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August 6 - 11, 2006

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

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Cited By

Pasupa KSeneewong Na Ayutthaya T(2021)Hybrid Deep Learning Models for Thai Sentiment AnalysisCognitive Computation10.1007/s12559-020-09770-014:1(167-193)Online publication date: 4-Mar-2021
https://doi.org/10.1007/s12559-020-09770-0
Elayeb BBounhas I(2016)Arabic Cross-Language Information RetrievalACM Transactions on Asian and Low-Resource Language Information Processing10.1145/278921015:3(1-44)Online publication date: 28-Jan-2016
https://dl.acm.org/doi/10.1145/2789210
Zhang LLi LLi T(2015)Patent MiningACM SIGKDD Explorations Newsletter10.1145/2783702.278370416:2(1-19)Online publication date: 21-May-2015
https://dl.acm.org/doi/10.1145/2783702.2783704
Mustafa MSuleman H(2015)Mixed Language Arabic-English Information RetrievalComputational Linguistics and Intelligent Text Processing10.1007/978-3-319-18117-2_32(427-447)Online publication date: 2015
https://doi.org/10.1007/978-3-319-18117-2_32
Kim SKo YOard D(2015)Combining lexical and statistical translation evidence for cross-language information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2315366:1(23-39)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1002/asi.23153
Ture FLin J(2014)Exploiting Representations from Statistical Machine Translation for Cross-Language Information RetrievalACM Transactions on Information Systems10.1145/264480732:4(1-32)Online publication date: 28-Oct-2014
https://dl.acm.org/doi/10.1145/2644807
Wu YOard D(2014)English and Chinese bilingual topic aspect classification: Exploring similarity measures, optimal LSA dimensions, and centroid correction of translated training examplesProceedings of the American Society for Information Science and Technology10.1002/meet.1450500103950:1(1-12)Online publication date: 8-May-2014
https://doi.org/10.1002/meet.14505001039
Wu YOard DBouthillier FJu B(2013)English and chinese bilingual topic aspect classificationProceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries10.5555/2655780.2655823(1-12)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.5555/2655780.2655823
Magdy WJones G(2013)Studying machine translation technologies for large-data CLIR tasks: a patent prior-art search case studyInformation Retrieval10.1007/s10791-013-9231-617:5-6(492-519)Online publication date: 21-Nov-2013
https://doi.org/10.1007/s10791-013-9231-6
Vulić IMoens M(2013)A unified framework for monolingual and cross-lingual relevance modeling based on probabilistic topic modelsProceedings of the 35th European conference on Advances in Information Retrieval10.1007/978-3-642-36973-5_9(98-109)Online publication date: 24-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-36973-5_9
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