Andrew Gordon: “Computational Approaches to the Analysis of Textual Stories”

February 15, 2008 | University of Pittsburgh

Speaker: Andrew Gordon
Host: University of Pittsburgh Computer Science Department

Narratives of real-world experiences (stories) are an effective vehicle for sharing information about events, but also tell us about the expectations of storytellers that were challenged by their experiences. Story-based learning environments and training simulations can capitalize on this quality to craft fictional experiences that challenge the expectations of the learner in same way. However, the potential of stories in knowledge sharing applications is limited by the degree to which the analysis of stories can be automated. In this talk, I will describe our ongoing efforts to automate the collection, analysis, and application of textual stories in large corpora (the web) using natural language processing technologies at different levels of representational abstraction. Machine learning methods operating at the word-level are used to segment stories from other text genres. Lexical-semantic analysis is used to model sequences of events and support commonsense inference. Finally, I will motivate the need for deep semantic interpretation, and describe our approach to the use of formal commonsense theories in story understanding.