Space and time representation in language is important in linguistics and cognitive science research, as well as artificial intelligence applications like conversational robots and navigation systems. This book is the first for linguists and computer scientists that shows how to do model-theoretic semantics for temporal or spatial information in natural language, based on annotation structures. The book covers the entire cycle of developing a specification for annotation and the implementation of the model over the appropriate corpus for linguistic annotation. Its representation language is a type-theoretic, first-order logic in shallow semantics. Each interpretation model is delimited by a set of definitions of logical predicates used in semantic representations (e.g., past) or measuring expressions (e.g., counts or k). The counting function is then defined as a set and its cardinality, involving a universal quantification in a model. This definition then delineates a set of admissible models for interpretation.