Stories are typically represented as a set of events and temporal or causal relations among events. In the metro map model of storylines, participants are represented as histories and events as interactions between participant histories. The metro map model calls for a decomposition of events into what each participant does (or what happens to each participant), as well as the interactions among participants. Such a decompositional model of events has been developed in linguistic semantics. Here, we describe this decompositional model of events and how it can be combined with a metro map model of storylines.

This chapter reviews the research conducted on the representation of events, from theperspectives ofnatural language processing, artificial intelligence (AI), and linguistics. AI approaches to modeling change have traditionally focused on situations and state descriptions. Linguistic approaches start with the description of the propositional content of sentences (or natural language expressions generally). As a result, the focus in the two fields has been on different problems. I argue that these approaches have common elements that can be drawn on to view event semantics from a unifying perspective, where we can distinguish between the surface events denoted by verbal predicates and what I refer to as the latent event structure of a sentence. By clearly distinguishing between surface and latent event structures of sentences and texts, we move closer to a general computational theory of event structure, one permitting a common vocabularyfor events and the relations between them, while enabling reasoning at multiple levels of interpretation.