Artificial Intelligence - Special volume on natural language processing
A Bayesian model of plan recognition
Artificial Intelligence
Procedural help in Andes: generating hints using a Bayesian network student model
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A computational architecture for conversation
UM '99 Proceedings of the seventh international conference on User modeling
Conflict resolution in collaborative planning dialogs
International Journal of Human-Computer Studies - Special issue on collaboration, cooperation and conflict in dialogue systems
The Architecture of Cognition
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Statistical source channel models for natural language understanding
Statistical source channel models for natural language understanding
A process model for recognizing communicative acts and modeling negotiation subdialogues
Computational Linguistics
A model for generating better explanations
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
A Probabilistic Approach for Argument Interpretation
User Modeling and User-Adapted Interaction
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Modeling suppositions in users' arguments
UM'05 Proceedings of the 10th international conference on User Modeling
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We present a probabilistic approach for the interpretation of arguments that casts the selection of an interpretation as a model selection task. In selecting the best model, our formalism balances conflicting factors: model complexity against data fit, and structure complexity against belief reasonableness. We first describe our basic formalism, which considers interpretations comprising inferential relations, and then show how our formalism is extended to suppositions that account for the beliefs in an argument, and justifications that account for the inferences in an interpretation. Our evaluations with users show that the interpretations produced by our system are acceptable, and that there is strong support for the postulated suppositions and justifications.