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NAACL
2007

Bayesian Inference for PCFGs via Markov Chain Monte Carlo

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Bayesian Inference for PCFGs via Markov Chain Monte Carlo
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 the 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 the Inside-Outside algorithm only produce a trivial grammar.
Mark Johnson, Thomas L. Griffiths, Sharon Goldwate
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NAACL
Authors Mark Johnson, Thomas L. Griffiths, Sharon Goldwater
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