Semantic interpretation and the resolution of ambiguity
Semantic interpretation and the resolution of ambiguity
Information-based syntax and semantics: Vol. 1: fundamentals
Information-based syntax and semantics: Vol. 1: fundamentals
Mechanisms of sentence processing: assigning roles to constituents
Parallel distributed processing: explorations in the microstructure of cognition, vol. 2
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The berkeley UNIX consultant project
Computational Linguistics
Theory of Syntactic Recognition for Natural Languages
Theory of Syntactic Recognition for Natural Languages
Concretion Inferences in NAtural Language Understanding
GWAI '87 Proceedings of the 11th German Workshop on Artificial Intelligence
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Representing and integrating linguistic knowledge
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Representing and integrating linguistic knowledge
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Estimating probability distributions over hypotheses with variable unification
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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In this paper, we describe a probabilistic framework for unification-based grammars that facilitates integrating syntactic and semantic constraints and preferences. We share many of the concerns found in recent work on massively-parallel language interpretation models, although the proposal reflects our belief in the value of a higher-level account that is not stated in terms of distributed computation. We also feel that inadequate learning theories severely limit existing massively-parallel language interpretation models. A learning theory is not only interesting in its own right, but must underlie any quantitative account of language interpretation, because the complexity of interaction between constraints and preferences makes ad hoc trial-and-error strategies for picking numbers infeasible, particularly for semantics in realistically-sized domains.