Investigating the Language of Uncertainty - experimental data, formal semantics & probabilistic pragmatics

This dissertation reports a series of studies about the language of uncertainty in English. We investigate what we call “uncertainty expressions”, which include both verbal probabilities such as probable and likely and epistemic modals such as might and possible. Moreover, we look at complex, or nested, uncertainty expressions such as certainly likely and might be possible. The issues investigated in this work lie at the interface between semantics and pragmatics, and we attempt to answer questions such as: How do speakers communicate under uncertainty and about uncertainty? And what is the uncertainty intuitively expressed by uncertainty expressions? What do uncertainty expressions actually mean? And what is the role of context when we use them in a conversation? Almost as frequent as logical connectives and quantifiers, uncertainty expressions are ubiquitous in everyday conversations. Unsurprisingly, uncertainty expressions have been extensively investigated by philosophers, logicians, linguists and cognitive scientists. One of the goals of this dissertation is to bring closer together the different traditional approaches to the study of uncertainty expressions. In our investigation, we strive for interdisciplinarity. In doing so, we integrate methods coming from formal semantics and pragmatics with experimental data and computational modeling. The focal point of the dissertation is a novel data-driven probabilistic model of the use and the interpretation of simple and complex uncertainty expressions

Modeling transfer of high-order uncertain information

Complex uncertainty expressions such as probably likely andcertainly possible naturally occur in everyday conversations.However, they received much less attention in the literaturethan simple ones. We propose a probabilistic model of the useand interpretation of complex uncertainty expressions based onthe assumption that their predominant function is to communi-cate factual information about the world, and that further layersof uncertainty are pragmatically inferred. We collected empir-ical data on the use and interpretation of these expressions anduse it for detailed model criticism

Analysis

Bayesian ordinal mixed effect regression models. LOO model comparison

Cleaning and visualizing data

Raw data, data cleaning, preliminary data visualizatio