Parameter estimation for constrained context-free language models

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.