The validity of semantic inferences depends on the contexts in which they are applied. We propose a generic framework for handling contextual considerations within applied inference, termed Contextual Preferences. This framework defines the various context-aware components needed for inference and their relationships. Contextual preferences extend and generalize previous notions, such as selectional preferences, while experiments show that the extended framework allows improving inference quality on real application data.