Adaptor grammars

A framework for specifying compositional nonparametric Bayesian models

Mark Johnson*, Thomas L. Griffiths, Sharon Goldwater

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

108 Citations (Scopus)

Abstract

This paper introduces adaptor grammars, a class of probabilistic models of language that generalize probabilistic context-free grammars (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with "adaptors" that can induce dependencies among successive uses. With a particular choice of adaptor, based on the Pitman-Yor process, nonparametric Bayesian models of language using Dirichlet processes and hierarchical Dirichlet processes can be written as simple grammars. We present a general-purpose inference algorithm for adaptor grammars, making it easy to define and use such models, and illustrate how several existing nonparametric Bayesian models can be expressed within this framework.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
EditorsBernhard Scholkopf, John Platt, Thomas Hofmann
Place of PublicationCambridge, MA
PublisherMIT Press
Pages641-648
Number of pages8
ISBN (Print)9780262195683
Publication statusPublished - 2007
Externally publishedYes
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: 4 Dec 20067 Dec 2006

Other

Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CountryCanada
CityVancouver, BC
Period4/12/067/12/06

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    Johnson, M., Griffiths, T. L., & Goldwater, S. (2007). Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models. In B. Scholkopf, J. Platt, & T. Hofmann (Eds.), Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference (pp. 641-648). Cambridge, MA: MIT Press.