This book highlights cutting-edge research relevant to the building of a computational model of reading comprehension, as in the processing and understanding of a natural language text or story. A distinguishing feature of the book is its emphasis on "real" understanding of "real" narrative texts rather than on syntactic parsing of single sentences taken out of context or on limited understanding of small, researcher-constructed stories.
The book takes an interdisciplinary approach to the study of reading, with contributions from computer science, psychology, and philosophy. Contributors cover the theoretical and psychological foundations of the research in discussions of what it means to understand a text, how one builds a computational model, and related issues in knowledge representation and reasoning. The book also addresses some of the broader issues that a natural language system must deal with, such as reading in context, linguistic novelty, and information extraction.
Dorrit Billman, Michael T. Cox, Eric Domeshek, Kurt Eiselt, Charles R. Fletcher, Richard Gerrig, Jennifer Holbrook, Eric Jones, Trent Lange, Mark Langston, Joe Magliano, Kavi Mahesh, Bonnie J. F. Meyer, Justin Peterson, William J. Rapaport, Ellen Riloff, Stuart C. Shapiro, Tom Trabasso, Charles M. Wharton.