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Learning phonetic similarity for matching named entity translations and mining new translations

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SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 289 - 296

https://doi.org/10.1145/1008992.1009043

Published: 25 July 2004 Publication History

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Abstract

We propose a novel named entity matching model which considers both semantic and phonetic clues. The matching is formulated as an optimization problem. One major component is a phonetic matching model which exploits similarity at the phoneme level. We investigate three learning algorithms for obtaining the similarity information of basic phoneme units based on training examples. By applying this proposed named entity matching model, we also develop a mining framework for discovering new, unseen named entity translations from online daily Web news. This framework harvests comparable news in different languages using an existing bilingual dictionary. It is able to discover new name translations not found in the dictionary.

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Cited By

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Liu YLi QSun CSi LDiaz FShah CSuel TCastells PJones RSakai T(2021)Similar Trademark Detection via Semantic, Phonetic and Visual Similarity InformationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463038(2025-2030)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463038
Li BYao J(2019)Study on Unknown Term Translation Mining from Google SnippetsInformation10.3390/info1009026710:9(267)Online publication date: 28-Aug-2019
https://doi.org/10.3390/info10090267
Prabhakar DPal S(2018)Machine transliteration and transliterated text retrieval: a surveySādhanā10.1007/s12046-018-0828-843:6Online publication date: 7-Jun-2018
https://doi.org/10.1007/s12046-018-0828-8
Show More Cited By

Index Terms

Learning phonetic similarity for matching named entity translations and mining new translations
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

July 2004

624 pages

ISBN:1581138814

DOI:10.1145/1008992

General Chair:
Mark Sanderson
University of Sheffield (UK)
,
Program Chairs:
Kalervo Järvelin
University of Tampere (Finland)
,
James Allan
University of Massachusetts (USA)
,
Peter Bruza
Distributed Systems Technology Centre (Australia)

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

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Published: 25 July 2004

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SIGIR04: The 27th ACM/SIGIR International Symposium on Information Retrieval 2004

July 25 - 29, 2004

Sheffield, United Kingdom

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

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Liu YLi QSun CSi LDiaz FShah CSuel TCastells PJones RSakai T(2021)Similar Trademark Detection via Semantic, Phonetic and Visual Similarity InformationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463038(2025-2030)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463038
Li BYao J(2019)Study on Unknown Term Translation Mining from Google SnippetsInformation10.3390/info1009026710:9(267)Online publication date: 28-Aug-2019
https://doi.org/10.3390/info10090267
Prabhakar DPal S(2018)Machine transliteration and transliterated text retrieval: a surveySādhanā10.1007/s12046-018-0828-843:6Online publication date: 7-Jun-2018
https://doi.org/10.1007/s12046-018-0828-8
Sudoh KMori SNagata M(2014)Noise-aware Character Alignment for Extracting Transliteration FragmentsJournal of Natural Language Processing10.5715/jnlp.21.110721:6(1107-1131)Online publication date: 2014
https://doi.org/10.5715/jnlp.21.1107
Karimi SScholer FTurpin A(2011)Machine transliteration surveyACM Computing Surveys10.1145/1922649.192265443:3(1-46)Online publication date: 29-Apr-2011
https://dl.acm.org/doi/10.1145/1922649.1922654
Liu LGe YYan ZYao J(2011)A CLIR-oriented OOV translation mining method from bilingual webpages2011 International Conference on Machine Learning and Cybernetics10.1109/ICMLC.2011.6016958(1872-1877)Online publication date: Jul-2011
https://doi.org/10.1109/ICMLC.2011.6016958
Guihua Sun Gaopan Xu Ke Zhang (2008)Translating OOV phrases based on lexical information and web mining2008 3rd International Conference on Intelligent System and Knowledge Engineering10.1109/ISKE.2008.4731037(791-796)Online publication date: Nov-2008
https://doi.org/10.1109/ISKE.2008.4731037
Zhao HRam S(2008)Entity matching across heterogeneous data sourcesData & Knowledge Engineering10.1016/j.datak.2008.04.00766:3(368-381)Online publication date: 1-Sep-2008
https://dl.acm.org/doi/10.1016/j.datak.2008.04.007
Kuo JLi HYang Y(2007)A phonetic similarity model for automatic extraction of transliteration pairsACM Transactions on Asian Language Information Processing10.1145/1282080.12820816:2(6-es)Online publication date: 1-Sep-2007
https://dl.acm.org/doi/10.1145/1282080.1282081
Lam WChan SHuang R(2007)Named entity translation matching and learningACM Transactions on Information Systems10.1145/1198296.119829825:1(2-es)Online publication date: 1-Feb-2007
https://dl.acm.org/doi/10.1145/1198296.1198298
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Named entity translation matching and learning: With application for mining unseen translations

Mining entity translations from comparable corpora: a holistic graph mapping approach

Extracting named entity translingual equivalence with limited resources

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