Translating Queries into Snippets for Improved Query Expansion


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.