The Semantics of Gradable Modal Expressions

我们使用语言不仅是用来交流我们周围发生的事情和实际发生的事情，而且还用来娱乐可能性，描述可能发生的事情或可能发生的事情，讲述什么是可取的，什么是必要的。情态表达，如“允许的”、“可能的”、“可能的”、“能够的”和“可能的”等，使我们可以在语言上进入非实际的领域。在过去的四十年里，语言学家和哲学家在理解情态表达的意义方面取得了长足的进步。然而，这一意义的一个方面却鲜为人知，那就是情态意义的可级性：可能性可以根据其可能性、可取性或允许性进行排名，可能性、可取性或允许性的程度通常可以衡量。我们不仅可以说“可能会下雪”，还可以说“很有可能会下雪”，“下雪的可能性有60%”，或者“下雪的可能性比下雨大”。这个项目旨在提供一个完整的情态理论，系统地解释可分级情态表达的意义，理解基本情态表达的意义以及该意义是如何受到语境的调节的。研究人员将利用形式模型和基于语料库的现代方法来研究在解释分级情态意义时语境和内容之间的复杂交互作用。通过巩固我们对可分级情态语言的理解，并将这种理解整合到当代关于不确定推理的工作中，本研究有望在部分信息下的自动推理和风险管理方面有重要的应用。

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.