A matching technique in Example-Based Machine Translation

  • Authors:
  • Lambros Cranias;Harris Papageorgiou;Stelios Piperidis

  • Affiliations:
  • Institute for Language and Speech Processing, Greece;Institute for Language and Speech Processing, Greece;Institute for Language and Speech Processing, Greece

  • Venue:
  • COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
  • Year:
  • 1994

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Abstract

This paper addresses an important problem in Example-Based Machine Translation (EMBT), namely how to measure similarity between a sentence fragment and a set of stored examples. A new method is proposed that measures similarity according to both surface structure and content. A second contribution is the use of clustering to make retrieval of the best matching example from the database more efficient. Results on a large number of test cases from the CELEX database are presented.