Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Automatic inference: a probabilistic basis for natural language interpretation
Automatic inference: a probabilistic basis for natural language interpretation
A Probabilistic Approach to Text Understanding
A Probabilistic Approach to Text Understanding
A logic for semantic interpretation
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
We analyze the difficulties in applying Bayesian belief networks to language interpretation domains, which typically involve many unification hypotheses that posit variable bindings. As an alternative, we observe that the structure of the underlying hypothesis space permits an approximate encoding of the joint distribution based on marginal rather than conditional probabilities. This suggests an implicit binding approach that circumvents the problems with explicit unification hypotheses, while still allowing hypotheses with alternative unifications to interact probabilistically. The proposed method accepts arbitrary subsets of hypotheses and marginal probability constraints, is robust, and is readily incorporated into standard unification-based and frame-based models.