Abstract
This article reviews some recently invented methods for automatically extracting translation lexicons from parallel texts. The accuracy of these methods has been significantly improved by exploiting known properties of parallel texts and of particular language pairs. The state of the art has advanced to the point where non-compositional compounds can be automatically identified with high reliability, and their translations can be found. Most importantly, all of these methods can be smoothly integrated into the usual work ow of MT system developers. Semi-automatic MT lexicon construction is likely to be more efficient and more accurate than either fully automatic or fully manual methods alone.
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Bibliography
I. D. Melamed. (1998) Empirical Methods for Exploiting Parallel Texts, Ph.D. dissertation. University of Pennsylvania, Philadelphia, PA.
I. D. Melamed. (to appear) “Bitext Maps and Alignment via Pattern Recognition,” to appear in Computational Linguistics.
I. D. Melamed. (submitted) “Word-to-Word Models of Translational Equivalence,” submitted to Computational Linguistics.
D. Yarowsky. (1993) “One Sense Per Collocation,” DARPA Workshop on Human Language Technology. Princeton, NJ.
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© 1998 Springer-Verlag Berlin Heidelberg
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Dan Melamed, I. (1998). Empirical Methods for MT Lexicon Development. In: Farwell, D., Gerber, L., Hovy, E. (eds) Machine Translation and the Information Soup. AMTA 1998. Lecture Notes in Computer Science(), vol 1529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49478-2_2
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DOI: https://doi.org/10.1007/3-540-49478-2_2
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