We use language not only to communicate about what immediately surrounds us and what actually takes place, but also to entertain possibilities and to describe what may happen or what is likely to occur, to recount what is desirable and what is necessary. Modal expressions such as 'permissible', 'probability', 'may', 'able', and 'likely' allow us to linguistically enter the realm of the non-actual. In the past forty years linguists and philosophers have made great strides in understanding the meaning of modal expressions. One aspect of that meaning that is poorly understood, however, is the gradability of modal meaning: Possibilities can be ranked in terms of their likelihood, their desirability, or their permissibility, and the degrees of likelihood, desirability, or permissibility can often be measured. We can say not only "It may snow," but also "There is a high probability that it'll snow," "There is a 60% probability that it'll snow," or "It is more likely to snow than to rain."
This project aims to offer an integrated theory of modality that accounts in a systematic way for the meaning of gradable modal expressions, understanding both the meaning of basic modal expressions as well as how that meaning is modulated by context. The researchers will make use of formal models as well as modern corpus-based methods to investigate the complex interactions between context and content in the interpretation of gradable modal meaning. By firmly grounding our understanding of the language of gradable modality and by integrating this understanding into contemporary work on uncertain inference, this research promises to have important applications to automatic reasoning under partial information and to risk management.
