A Language for Construction of Belief Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic model of plan recognition
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Network engineering for complex belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Relevant explanations: allowing disjunctive assignments
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Objection-based causal networks
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
A probabilistic analysis of marker-passing techniques for plan-recognition
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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We discuss a new framework for text understanding. Three major design decisions characterize this approach. First, we take the problem of text understanding to be a particular case of the general problem of abductive inference: reasoning from effects to causes. Second, we use probability theory to handle the uncertainty which arises in abductive inference in general, and natural language understanding in particular. Finally, we treat all aspects of the text understanding problem in a unified way. All aspects of natural language processing are treated in the same framework, allowing us to integrate syntactic, semantic and pragmatic constraints. In order to apply probability theory to this problem, we have developed a probabilistic model of text understanding. To make it practical to use this model, we have devised a way of incrementally constructing and evaluating belief networks that is applicable to other abduction problems. We have written a program, Wimp3, to experiment with this framework.