User logs of search engines have recently been applied successfully to improve
various aspects of web search quality. In this paper, we will apply pairs of user
queries and snippets of clicked results to train a machine translation model to
bridge the ``lexical gap'' between query and document space. We show that the
combination of a query-to-snippet translation model with a large n-gram language
model trained on queries achieves improved contextual query expansion compared to a
system based on term correlations.