There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from documents. These systems operate on structured verb-argument events produced by an NLP pipeline. We compare these systems with recent Recurrent Neural Net models that directly operate on raw tokens to predict sentences, finding the latter to be roughly comparable to the former in terms of predicting missing events in documents.
For both people and machines, each in their own way, there is a serious problem in common of making sense out of what they hear, see, or are told about the world. The conceptual apparatus necessary to perform even a partial feat of understanding is formidable and fascinating. Our analysis of this apparatus is what this book is about. Roger C. Schank and Robert P. Abelson from the Introduction (http: www.psypress.com scripts-plans-goals-and-understanding-9780898591385) Abstract : A partial theory is presented of thinking, combining a number of classical and modern concepts from psychology, linguistics, and AI. In a new situation one selects from memory a structure called a frame: a remembered framework to be adapted to fit reality by changing details as necessary, and a data-structure for representing a stereotyped situation. Attached to each frame are several kinds of information -- how to use the frame, what one can expect to happen next, and what to do if these expectations are not confirmed. The report discusses collections of related frames that are linked together into frame-systems.