论文信息 - Using a Support-Vector Machine for Japanese-to-English Translation of Tense, Aspect, and Modality

Using a Support-Vector Machine for Japanese-to-English Translation of Tense, Aspect, and Modality

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.

[1] Masaki Murata,et al. An Example-Based Approach to Japanese-to-English Translation of Tense Aspect, and Modality , 1999, ArXiv.

[2] Masaki Murata,et al. Bunsetsu Identification Using Category-Exclusive Rules , 2000, COLING.

[3] Masaki Murata,et al. Correction of Errors in a Modality Corpus Used for Machine Translation by Using Machine-learning Method , 2001, ArXiv.

[4] Eric Sven Ristad,et al. Maximum Entropy Modeling Toolkit , 1996, ArXiv.

[5] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[6] Masahiko Haruno,et al. Feature Selection in SVM Text Categorization , 1999, AAAI/IAAI.

[7] Satoshi Sekine,et al. The Domain Dependence of Parsing , 1997, ANLP.

[8] Yuji Matsumoto,et al. Use of Support Vector Learning for Chunk Identification , 2000, CoNLL/LLL.