Inference for PCFGs and Adaptor Grammars

author: Mark Johnson, Brown Laboratory for Linguistic Information Processing, Brown University
published: Jan. 19, 2010,   recorded: December 2009,   views: 4915


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This talk describes the procedures we've developed for adaptor grammar inference. Adaptor grammars are a non-parametric extension to PCFGs that can be used to describe a variety of phonological and morphological language learning tasks. We start by reviewing an MCMC sampler for Probabilistic Context-Free Grammars that serves as the basis for adaptor grammar inference, and then explain how samples from a PCFG whose rules depend on the other sampled trees can be used as a proposal distribution in an MCMC procedure for estimating adaptor grammars. Finally we describe several optimizations that dramatically speed inference of complex adaptor grammars.

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Reviews and comments:

Comment1 SvenTaow, April 22, 2019 at 1:08 p.m.:

Where can I find the samples of morphological language learning tasks? I'm writing a paper on Grammar with reference to Achieve Grammar tools at and other important Achieve linguistics theory resources. Morphology was my major at college and I applied to become a scientist at Brigham Young University. Now, I'm an associate linguist here.

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