This study investigated the automatic modelling of space and time in narratives involving dining in a restaurant. We built a program that (1) uses information extraction techniques to convert narrative texts into templates containing key information about the dining episodes discussed in the narratives, (2) constructs commonsense reasoning problems from the templates, (3) uses commonsense reasoning and a commonsense knowledge base to build models of the dining episodes, and (4) generates and answers questions by consulting the models. We describe the program and present the results of running it on a corpus of web texts and American literature.
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