A statistical approach to machine translation
Computational Linguistics
MBT2: a method for combining fragments of examples in example-based translation
Artificial Intelligence - Special issue: AI research in Japan
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
A Japanese Input Method for Mobile Terminals Using Surface EMG Signals
New Frontiers in Artificial Intelligence
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On our proposed method, source language is translated into target language via Number Representation. A text in the source language is translated into a number representation text. The number representation text is the number string corresponding to the original source language text. The number representation text is translated into a number representation text for the target language. The number representation text is translated into a text in the target language. The text is the translation result finally. A number representation text is more abstract than the original text because the number representation text corresponds to several texts. The system based on our proposed method is able to acquire more translation rules on number representation than that on the original text by Inductive Learning. Moreover, the system disambiguates number representation by its own adaptability. In the experiment, the correct translation rate for our proposed method is higher than that for the method without number representation. Thus, it is proved that our proposed method is more effective for machine translation.