JOURNAL ARTICLE

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

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.

Keywords:
Modality (human–computer interaction) Computer science Support vector machine Artificial intelligence Variety (cybernetics) Machine translation Translation (biology) Natural language processing

Metrics

12
Cited By
1.32
FWCI (Field Weighted Citation Impact)
7
Refs
0.85
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Text and Document Classification Technologies
Physical Sciences →  Computer Science →  Artificial Intelligence
Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Rough Sets and Fuzzy Logic
Physical Sciences →  Computer Science →  Computational Theory and Mathematics

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