In order to estimate the parameters of our model , we develop a blocked sampler based on that of Johnson et al. ( 2007 ) to sample parse trees for sentences in the raw training corpus according to their posterior probabilities .
bayesian inference for pcfgs via markov chain monte carlo
Mark Johnson Thomas Griffiths Sharon Goldwater
In this paper , we seek to learn from only raw data and an incomplete dictionary mapping some words to sets of potential supertags . In order to estimate the parameters of our model , we develop a blocked sampler based on that of Johnson et al. ( 2007 ) to sample parse trees for sentences in the raw training corpus according to their posterior probabilities . However , due to the very large sets of potential supertags used in a parse , computing inside charts is intractable , so we design a Metropolis-Hastings step that allows us to sample efficiently from the correct posterior .
In order to estimate the parameters of our model , we develop a blocked sampler based on that of @@CITATION to sample parse trees for sentences in the raw training corpus according to their posterior probabilities .