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首页> 外文期刊>電子情報通信学会技術研究報告. 言語理解とコミュニケーション. Natural Language Understanding and Models of Communication >Using a support-vector machine in the Japanese-to-English translation of tense, aspect, and modality
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Using a support-vector machine in the Japanese-to-English translation of tense, aspect, and modality

机译：在时态，方面和情态的日语-英语翻译中使用支持向量机

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摘要

Tense, aspect, and modality are known to present difficult problems in machine translation. In traditional approaches, tense, aspect, and modality have been translated by using manually constructed heuristic rules. Recently, however, such corpus-based approach as the k-nearest neighborhood method have also been applied. This paper is a report on experiments we carried out on the application of a variety of machine-learning methods, including the k-nearest neighborhood, to the translation of tense, aspect, and modality. One experimental result was that support vector machine obtained the highest precisions of the methods we applied. In the previous work, applying the k-nearest neighborhood method, only those strings at the ends of sentences were used for the translation of tense, aspect, and modality. In contrast, our method used all morphemes of the whole sentences as information and the support vector machine thus obtained a higher precision than it did by using the ends of sentences. We therefore found that using all of the morphemes of a whole sentence is effective in the translation of tense, aspect, and modality.
机译：已知时态，方面和形式会在机器翻译中带来难题。在传统方法中，时态，方面和形式已通过使用人工构造的启发式规则进行翻译。然而，近来，也已经应用了诸如k近邻法的基于语料库的方法。本文是关于我们在将k近邻法等多种机器学习方法应用于时态，方面和情态翻译时进行的实验的报告。一个实验结果是支持向量机获得了我们所应用方法的最高精度。在先前的工作中，应用k最近邻法，仅将句子结尾处的那些字符串用于时态，方面和情态的翻译。相反，我们的方法将整个句子的所有词素用作信息，因此支持向量机比使用句子结尾获得更高的精度。因此，我们发现使用整个句子的所有词素对时态，方面和情态的翻译都是有效的。

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