The majority of the current information retrieval models weight the concepts in the search query (e.g., query terms or phrases) in an unsupervised manner, based solely on the collection statistics. In this talk, I will present a parameterized concept weighting framework that goes beyond the unsupervised estimation of query concept weights. In this framework, the weight of each query concept is automatically learned using a parameterized combination of diverse importance features. In addition, the proposed framework is able to learn importance weights not only for the concepts that are explicitly present in the query, but also for the latent concepts that can be associated with the query through several query expansion techniques. Extensive empirical evaluation demonstrates that the parameterized concept weighting consistently and significantly outperforms a wide range of state-of-the-art retrieval models.