Coping With Ambiguity in a Large-Scale Machine Translation System

نویسندگان

  • Kathryn L. Baker
  • Alexander Franz
  • Pamela W. Jordan
  • Teruko Mitamura
  • Eric Nyberg
چکیده

In an interlingual knowledge-based machine translation system, ambiguity arises when the source language 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 methods used 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 reduce the average number of parses per sentence.

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تاریخ انتشار 1994