Bayesian Inference for PCFGs via Markov Chain Monte Carlo
Mark S. Johnson, Thomas L. Griffiths, Sharon J. Goldwater · 2007
This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximum-likelihood estimation using Inside-Outside algorithm. We illustrate these methods by estimating a sparse grammar describing the morphology of the Bantu language Sesotho, demonstrating that with suitable priors Bayesian techniques can infer linguistic structure in situations where maximum likelihood methods such as Inside-Outside algorithm only produce a trivial grammar.