This paper focuses on the question of what kind of data needs to be recorded about figurative language, in order to capture the essential meaning of the text and to enable us to re-create a synonymous text, based on that data. A short review of the best known systems of semantic annotation will be presented and their suitability for the task will be analyzed. Also, a method that could be used for representing the meaning of the idioms, metaphors and metonymy in the data model will be considered.

Type theory has played an important role in specifying the formal connection between syntactic structure and semantic interpretation within the history of formal semantics. In recent years rich type theories developed for the semantics of programming languages have become influential in the semantics of natural language. The use of probabilistic reasoning to model human learning and cognition has become an increasingly important part of cognitive science. In this paper we offer a probabilistic formulation of a rich type theory, Type Theory with Records (TTR), and we illustrate how this framework can be used to approach the problem of semantic learning. Our probabilistic version of TTR is intended to provide an interface between the cognitive process of classifying situations according to the types that they instantiate, and the compositional semantics of natural language.