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Extracting word sequence correspondences with support vector machines

Authors:

Kengo Sato,

Hiroaki SaitoAuthors Info & Claims

COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1

Pages 1 - 7

https://doi.org/10.3115/1072228.1072248

Published: 24 August 2002 Publication History

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Abstract

This paper proposes a learning and extracting method of word sequence correspondences from non-aligned parallel corpora with Support Vector Machines, which have high ability of the generalization, rarely cause over-fit for training samples and can learn dependencies of features by using a kernel function. Our method uses features for the translation model which use the translation dictionary, the number of words, part-of-speech, constituent words and neighbor words. Experiment results in which Japanese and English parallel corpora are used archived 81.1% precision rate and 69.0% recall rate of the extracted word sequence correspondences. This demonstrates that our method could reduce the cost for making translation dictionaries.

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Cited By

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Chen PLin S(2011)Word AdHoc NetworkKnowledge-Based Systems10.1016/j.knosys.2010.11.00624:3(393-405)Online publication date: 1-Apr-2011
https://dl.acm.org/doi/10.1016/j.knosys.2010.11.006
Zhang LZhang DSimoff SDebenham J(2006)Weighted kernel model for text categorizationProceedings of the fifth Australasian conference on Data mining and analystics - Volume 6110.5555/1273808.1273823(111-114)Online publication date: 1-Nov-2006
https://dl.acm.org/doi/10.5555/1273808.1273823
Aramaki EKurohashi SKashioka HTanaka H(2004)Example-Based machine translation without saying inferable predicateProceedings of the First international joint conference on Natural Language Processing10.1007/978-3-540-30211-7_22(206-215)Online publication date: 22-Mar-2004
https://dl.acm.org/doi/10.1007/978-3-540-30211-7_22

Extracting word sequence correspondences with support vector machines
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1

August 2002

1184 pages

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Published: 24 August 2002

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Chen PLin S(2011)Word AdHoc NetworkKnowledge-Based Systems10.1016/j.knosys.2010.11.00624:3(393-405)Online publication date: 1-Apr-2011
https://dl.acm.org/doi/10.1016/j.knosys.2010.11.006
Zhang LZhang DSimoff SDebenham J(2006)Weighted kernel model for text categorizationProceedings of the fifth Australasian conference on Data mining and analystics - Volume 6110.5555/1273808.1273823(111-114)Online publication date: 1-Nov-2006
https://dl.acm.org/doi/10.5555/1273808.1273823
Aramaki EKurohashi SKashioka HTanaka H(2004)Example-Based machine translation without saying inferable predicateProceedings of the First international joint conference on Natural Language Processing10.1007/978-3-540-30211-7_22(206-215)Online publication date: 22-Mar-2004
https://dl.acm.org/doi/10.1007/978-3-540-30211-7_22

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Extracting word correspondences from bilingual corpora based on word co-occurrences information

Selecting the best feature set for Thai word sense disambiguation using support vector machines

Detecting errors in corpora using support vector machines

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