Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models

Mark Johnson, Thomas L. Griffiths, Sharon Goldwater

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

17 Scopus citations

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 (US)
Title of host publicationNIPS 2006
Subtitle of host publicationProceedings of the 19th International Conference on Neural Information Processing Systems
EditorsBernhard Scholkopf, John C. Platt, Thomas Hofmann
PublisherMIT Press Journals
Pages641-648
Number of pages8
ISBN (Electronic)0262195682, 9780262195683
StatePublished - 2006
Externally publishedYes
Event19th International Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, Canada
Duration: Dec 4 2006Dec 7 2006

Publication series

NameNIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems

Conference

Conference19th International Conference on Neural Information Processing Systems, NIPS 2006
Country/TerritoryCanada
CityVancouver
Period12/4/0612/7/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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