Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian model of plan recognition
Artificial Intelligence
A computational architecture for conversation
UM '99 Proceedings of the seventh international conference on User modeling
An information-theoretic approach for argument interpretation in a conversational setting
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A Probabilistic Approach for Argument Interpretation
User Modeling and User-Adapted Interaction
Lexical paraphrasing for document retrieval and node identification
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Incorporating a user model into an information theoretic framework for argument interpretation
UM'03 Proceedings of the 9th international conference on User modeling
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We describe a mechanism for the interpretation of arguments, which can cope with noisy conditions in terms of wording, beliefs and argument structure. This is achieved through the application of the Minimum Message Length Principle to evaluate candidate interpretations. Our system receives as input a quasi-Natural Language argument, where propositions are presented in English, and generates an interpretation of the argument in the form of a Bayesian network (BN). Performance was evaluated by distorting the system's arguments (generated from a BN) and feeding them to the system for interpretation. In 75% of the cases, the interpretations produced by the system matched precisely or almost-precisely the representation of the original arguments.