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Coping with Ambiguity in a Large-scale Machine Translation System by Kathryn L. Baker; Alexander M. Franz; Pamela W. Jordan; Teruko Mitamura; Eric H. Nyberg is a scholarly article available to read on EtoBox.

What is Coping with Ambiguity in a Large-scale Machine Translation System about?

In an interlingual knowledge-based machine translation system, ambiguity arises when the source 1.qnguage analyzer produces more than one interlingua expression for a source sentence. This can have a negative impact on translation quality, since a target sentence may be produced from an unintended meaning. In this paper we describe the ,nethods nsed in the KANT machine translation system to reduce or eliminate ambiguity in a large-scale application domain. We also test these methods on a large corpus of test sentences, in order to illustrate how the different disambiguation methods redtuce the average number of parses per sentence,

Author

Kathryn L. Baker; Alexander M. Franz; Pamela W. Jordan; Teruko Mitamura; Eric H. Nyberg

Publisher

Association for Computational Linguistics

Published

1994

Language

EN