Parameter estimation for constrained context-free language models

Authors:
Kevin Mark;Michael Miller;Ulf Grenander;Steve Abney
Affiliations:
Washington University, St. Louis, Missouri;Washington University, St. Louis, Missouri;Washington University, St. Louis, Missouri;Washington University, St. Louis, Missouri
Venue:
HLT '91 Proceedings of the workshop on Speech and Natural Language
Year:
1992

Citing 0
Cited 5

Stochastic attribute-value grammars

Computational Linguistics
Statistical properties of probabilistic context-free grammars

Computational Linguistics
Integrated techniques for phrase extraction from speech

HLT '94 Proceedings of the workshop on Human Language Technology
Treebank transfer

Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Statistical language modeling combining N-gram and context-free grammars

ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new language model incorporating both N-gram and context-free ideas is proposed. This constrained context-free model is specified by a stochastic context-free prior distribution with N-gram frequency constraints. The resulting distribution is a Markov random field. Algorithms for sampling from this distribution and estimating the parameters of the model are presented.