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Better contextual translation using
machine learning
October 1, 2002
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BibTex
Authors

Arul Menezes
Publication Type
Inproceedings
Publisher
Springer-Verlag
All copyrights reserved by Springer 2002.
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Abstract
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Related Info
Abstract
One of the problems facing translation systems that automatically extract transfer mappings
(rules or examples) from bilingual corpora is the trade-off between contextual specificity and
general applicability of the mappings, which typically results in conflicting mappings without
distinguishing context. We present a machine-learning approach to choosing between such
mappings, using classifiers that, in effect, selectively expand the context for these mappings
using features available in a linguistic representation of the source language input. We show that
using these classifiers in our machine translation system significantly improves the quality of the
translated output. Additionally, the set of distinguishing features selected by the classifiers
provides insight into the relative importance of the various linguistic features in choosing the
correct contextual translation.
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