Abstract
Knowledge acquisition is a critical problem for machine translation and translation selection. In this paper, I propose a tranlsation selection method that combines variable features from multiple language resources using machine learning. I introduce multiple measures for sense disambiguation and word selection that are based on language resources, and apply machine learning to combine those measures for translation selection. In evaluation, precision of translation selection improves even though a small-sized bilingual corpus is used as training data.
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Lee, H.A. (2006). Translation Selection Through Machine Learning with Language Resources. In: Matsumoto, Y., Sproat, R.W., Wong, KF., Zhang, M. (eds) Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead. ICCPOL 2006. Lecture Notes in Computer Science(), vol 4285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11940098_38
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DOI: https://doi.org/10.1007/11940098_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49667-0
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