To incorporate query information in the combination model, this thesis develops a number of query analysis models that can automatically discover mixing structure of the query space based on previous retrieval results, and predict combination parameters for unseen queries. In more detail, we propose the query-class based analysis model which needs to manually define the query classes and a series of probabilistic latent query analysis(pLQA) models which can automatically discover latent query classes from the development data by unifying the combination weight optimization and query class categorization into a discriminative learning framework. To adapt the combination function on a per query basis, this thesis also presents a probabilistic local context analysis(pLCA) model to automatically leverage additional retrieval sources to improve initial retrieval outputs. A pLCA variant is proposed to utilize human feedback to adjust combination parameters. 
