The Role of Event-Based Representations and Reasoning in Language

. This chapter briefly reviews the research conducted on the representation of events, from the perspectives of natural 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. Namely, linguistic theories try to maintain compositionality in the expressions associated with linguistic units, or what is known as semantic compositionality . In AI and in the planning community in particular the focus has been on maintaining compositionality in the way plans are constructed, as well as the correctness of the algorithm that searches and traverses the state space. This can be called plan compositionality . 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. Latent events within a text refer to the finer-grained subeventual representations of events denoted by verbs or nominal expressions, as well as to hidden events connoted by nouns. 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 vocabulary for events and the relations between them, while enabling reasoning at multiple levels of interpretation.