Using a support-vector machine for Japanese-to-English translation of tense, aspect, and modality

Authors:
Masaki Murata;Kiyotaka Uchimoto;Qing Ma;Hitoshi Isahara
Affiliations:
Communications Research Laboratory, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan;Communications Research Laboratory, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan;Communications Research Laboratory, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan;Communications Research Laboratory, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
Venue:
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
Year:
2001

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Abstract

This paper describes experiments carried out using a variety of machine-learning methods, including the k-nearest neighborhood method that was used in a previous study, for the translation of tense, aspect, and modality. It was found that the support-vector machine method was the most precise of all the methods tested.