Optimal Probabilistic Evaluation Functions for Search Controlled by Stochastic Context-Free Grammars
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Semirings and formal power series: their relevance to formal languages and automata
Handbook of formal languages, vol. 1
Parsing inside-out
Stochastic lexicalized tree-adjoining grammars
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Computational complexity of probabilistic disambiguation by means of tree-grammars
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
| Hi-index | 0.00 |
The purpose of this paper is (1) to provide a theoretical justification for the use of Monte-Carlo sampling for approximate resolution of NP-hard maximization problems in the framework of weighted parsing, and (2) to show how such sampling techniques can be efficiently implemented with an explicit control of the error probability. We provide an algorithm to compute the local sampling probability distribution that guarantee that the global sampling probability indeed corresponds to the aimed theoretical score. The proposed sampling strategy significantly differs from existing methods, showing by the same way the bias induced by these methods.