  This paper addresses an important problem in Example-Based Machine
Translation (EBMT), 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.
