A Matching Technique in Example-Based Machine Translation

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.

• Publication year

  1994

• Venue

  International Conference on Computational Linguistics

• Publication date

  1994-08-05

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/991886.991901arXiv cmp-lg/9508005
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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