Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models

Mark Johnson; Thomas L. Griffiths; Sharon Goldwater

Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models

Mark Johnson, Thomas L. Griffiths, Sharon Goldwater

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

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 language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Pages	641-648
Number of pages	8
State	Published - 2007
Externally published	Yes
Event	20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada Duration: Dec 4 2006 → Dec 7 2006

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Other

Other	20th Annual Conference on Neural Information Processing Systems, NIPS 2006
Country/Territory	Canada
City	Vancouver, BC
Period	12/4/06 → 12/7/06

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models",

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.",

author = "Mark Johnson and Griffiths, {Thomas L.} and Sharon Goldwater",

year = "2007",

language = "English (US)",

isbn = "9780262195683",

series = "Advances in Neural Information Processing Systems",

pages = "641--648",

booktitle = "Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference",

note = "20th Annual Conference on Neural Information Processing Systems, NIPS 2006 ; Conference date: 04-12-2006 Through 07-12-2006",

}

Johnson, M, Griffiths, TL & Goldwater, S 2007, Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models. in Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference. Advances in Neural Information Processing Systems, pp. 641-648, 20th Annual Conference on Neural Information Processing Systems, NIPS 2006, Vancouver, BC, Canada, 12/4/06.

Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models. / Johnson, Mark; Griffiths, Thomas L.; Goldwater, Sharon.
Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference. 2007. p. 641-648 (Advances in Neural Information Processing Systems).

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

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AU - Goldwater, Sharon

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Advances in Neural Information Processing Systems

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BT - Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference

Y2 - 4 December 2006 through 7 December 2006

ER -

Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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