TransBooster is a wrapper technology designed to improve the performance of wide-coverage machine translation systems. Using linguistically motivated syntactic information, it automatically decomposes source language sentences into shorter and syntactically simpler chunks, and recomposes their translation to form target language sentences. This generally improves both the word order and lexical selection of the translation. To date, TransBooster has been successfully applied to rule-based MT, statistical MT, and multi-engine MT. This paper presents the application of TransBooster to Example-Based Machine Translation. In an experiment conducted on test sets extracted from Europarl and the Penn II Treebank we show that our method can raise the BLEU score up to 3.8% relative to the EBMT baseline. We also conduct a manual evaluation, showing that TransBooster-enhanced EBMT produces a better output in terms of fluency than the baseline EBMT in 55% of the cases and in terms of accuracy in 53% of the cases.