This project probes the nature of psychological representations of discourse and applies the results to a computational model of "who knows what" in text about multiple agents. Many kinds of inferences and pragmatic effects depend on knowing who has access to which information, as well as who shares common ground. Computational and psychological models of text understanding have largely ignored the ramifications of multiple agent sources on the representation and use of textual information. Our objectives are: A) to examine how people process and represent the distinct knowledge states of agents depicted or quoted in both constructed and naturally occurring texts, B) to investigate how readers' representations of texts are affected by the perspectives they bring to these texts, as well as by how sources are presented, and C) to apply these empirical results to the construction of a partly-automated system for extracting, representing, and coding "who knows what" in a large corpus of naturally occurring texts - news stories from The Wall Street Journal. This research will contribute to practical goals concerning the modeling and extraction of information from text and reported speech, as well as to theoretical goals concerning how people process and represent texts about multiple agents.
