Clustering: a technique for search space reduction in example-based machine translation

Abstract

This work addresses an important problem in Example-Based Machine Translation (EBMT), namely how to make retrieval of the example that best matches the input more efficient. The use of clustering is proposed, to enable the application of the same similarity metric to first limit the search space and then locate the best available match in a database. Evaluation results are presented on a large number of test cases.