Temporal expressions, such as between 1994 and 2000, are frequent across many kinds of documents. Text retrieval, though, treats them as common terms, thus ignoring their inherent semantics. For queries with a strong temporal component, such as U.S. president 1999, this is problematic, since documents can not be reliably matched to the query. Our key contribution in this work is a novel approach that integrates temporal expressions in the language modeling framework, thus turning them into first-class citizens of the retrieval model. In addition, we present experimental evidence showing an improvement of retrieval effectiveness.