In the networked world of the information age, we are exposed to inordinate amounts of information. Search engines and information retrieval systems seek to discern the relevant from the irrelevant information given the context of a user's query. In this paper, we describe a system named Glean, which is based on the idea that coherent textcontains signi cant latent information, such as syntactic structure and patterns of language use, which can be used to enhance the performance of information retrieval systems. We propose a trainable approachthat makes use of syntactic information to increase the precision of information retrieval systems. We present results on these improvements to precision under di erent scenarios: using syntactic information at di erent granularity, and di erent sizes of syntactic contexts.