A Probabilistic Approach to Argument Interpretation
I will describe a probabilistic argument-interpretation mechanism embedded in a conversational system. The system is implemented as a detective game, where the user explores a virtual scenario, and constructs an argument for a suspect's guilt or innocence. The interpretation mechanism receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation -- a Bayesian network. Our mechanism uses an anytime algorithm to propose candidate interpretations, and selects the interpretation with the highest posterior probability. The results of our evaluation are encouraging, with the system generally producing plausible interpretations of users' arguments.
Bio: Ingrid Zukerman is an Associate Professor in Computer Science at Monash University. She received her B.Sc. degree in Industrial Engineering and Management and her M.Sc. degree in Operations Research from the Technion -- Israel Institute of Technology. She received her Ph.D. degree in Computer Science from UCLA in 1986. Since then, she has been working in the Faculty of Information Technology at Monash University. Her areas of interest are natural language processing and user modeling.