Bayesian inference for PCFGS via Markov Chain Monte Carlo

Mark Johnson; Thomas L. Griffiths; Sharon Goldwater

Bayesian inference for PCFGS via Markov Chain Monte Carlo

Mark Johnson^*, Thomas L. Griffiths, Sharon Goldwater

^*Corresponding author for this work

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

120 Citations (Scopus)

Abstract

This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximum-likelihood estimation using the Inside-Outside algorithm. We illustrate these methods by estimating a sparse grammar describing the morphology of the Bantu language Sesotho, demonstrating that with suitable priors Bayesian techniques can infer linguistic structure in situations where maximum likelihood methods such as the Inside-Outside algorithm only produce a trivial grammar.

Original language	English
Title of host publication	NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference
Place of Publication	Stroudsburg, PA
Publisher	Association for Computational Linguistics (ACL)
Pages	139-146
Number of pages	8
Publication status	Published - 2007
Externally published	Yes
Event	Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007 - Rochester, NY, United States Duration: 22 Apr 2007 → 27 Apr 2007

Other

Other	Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007
Country	United States
City	Rochester, NY
Period	22/04/07 → 27/04/07

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Johnson, M., Griffiths, T. L., & Goldwater, S. (2007). Bayesian inference for PCFGS via Markov Chain Monte Carlo. In NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference (pp. 139-146). Stroudsburg, PA: Association for Computational Linguistics (ACL).

Johnson, Mark ; Griffiths, Thomas L. ; Goldwater, Sharon. / Bayesian inference for PCFGS via Markov Chain Monte Carlo. NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference. Stroudsburg, PA : Association for Computational Linguistics (ACL), 2007. pp. 139-146

@inproceedings{ab4f0278a88f42739be77ede9851dbce,

title = "Bayesian inference for PCFGS via Markov Chain Monte Carlo",

abstract = "This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximum-likelihood estimation using the Inside-Outside algorithm. We illustrate these methods by estimating a sparse grammar describing the morphology of the Bantu language Sesotho, demonstrating that with suitable priors Bayesian techniques can infer linguistic structure in situations where maximum likelihood methods such as the Inside-Outside algorithm only produce a trivial grammar.",

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

year = "2007",

language = "English",

pages = "139--146",

booktitle = "NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference",

publisher = "Association for Computational Linguistics (ACL)",

}

Johnson, M, Griffiths, TL & Goldwater, S 2007, Bayesian inference for PCFGS via Markov Chain Monte Carlo. in NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference. Association for Computational Linguistics (ACL), Stroudsburg, PA, pp. 139-146, Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007, Rochester, NY, United States, 22/04/07.

Bayesian inference for PCFGS via Markov Chain Monte Carlo. / Johnson, Mark; Griffiths, Thomas L.; Goldwater, Sharon.

NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference. Stroudsburg, PA : Association for Computational Linguistics (ACL), 2007. p. 139-146.

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

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AU - Griffiths, Thomas L.

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AB - This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximum-likelihood estimation using the Inside-Outside algorithm. We illustrate these methods by estimating a sparse grammar describing the morphology of the Bantu language Sesotho, demonstrating that with suitable priors Bayesian techniques can infer linguistic structure in situations where maximum likelihood methods such as the Inside-Outside algorithm only produce a trivial grammar.

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BT - NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference

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