Memory-based Learning of Word Translation
نویسنده
چکیده
A basic task in machine translation is to choose the right translation for source words with several possible translations in the target language. In this paper we treat word translation as a word sense disambiguation problem and train memory-based classifiers on words with alternative translations. The training data was automatically labeled with the corresponding translations by word-aligning a parallel corpus. Results show that many words were translated with accuracy above the baseline.
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