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Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning

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Mark JohnsonAuthors Info & Claims

The Journal of Machine Learning Research, Volume 12

Pages 1425 - 1428

Published: 01 July 2011 Publication History

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Abstract

Grammar induction refers to the process of learning grammars and languages from data; this finds a variety of applications in syntactic pattern recognition, the modeling of natural language acquisition, data mining and machine translation. This special topic contains several papers presenting some of recent developments in the area of grammar induction and language learning, as applied to various problems in Natural Language Processing, including supervised and unsupervised parsing and statistical machine translation.

References

[1]

D. Angluin. Queries and concept learning. Machine Learning, 2:319-342, 1988.

Crossref

Google Scholar

[2]

W. S. Bennett and J. Slocum. The lrc machine translation system. Computational Linguistics, 11: 111-121, 1985.

Crossref

Google Scholar

[3]

S. B. Cohen and N. A. Smith. Covariance in unsupervised learning of probabilistic grammars. Journal of Machine Learning Research, 11:3017-3051, 2010.

Crossref

Google Scholar

[4]

T. Cohn, P. Blunsom, and S. Goldwater. Inducing tree-substitution grammars. Journal of Machine Learning Research, 11:3053-3096, 2010.

Crossref

Google Scholar

[5]

J. Gillenwater, K. Ganchev, J. Graça, F. Pereira, and B. Taskar. Posterior sparsity in unsupervised dependency parsing. Journal of Machine Learning Research, 11, 2011.

Crossref

Google Scholar

[6]

J. Graça, K. Ganchev, and B. Taskar. Expectation maximization and posterior constraints. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, Cambridge, MA, 2007. MIT Press.

Google Scholar

[7]

J. Henderson and I. Titov. Incremental sigmoid belief networks for grammar learning. Journal of Machine Learning Research, 11:3541-3570, 2010.

Crossref

Google Scholar

[8]

Y. Ni, C. Saunders, S. Szedmak, and M. Niranjan. Exploitation of machine learning techniques in modelling phrase movements for machine translation. Journal of Machine Learning Research, 12:1-30, 2011.

Crossref

Google Scholar

Cited By

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Yang RWang YWang BGong L(2019)Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical RecordsIntelligent Computing Methodologies10.1007/978-3-030-26766-7_22(235-243)Online publication date: 3-Aug-2019
https://dl.acm.org/doi/10.1007/978-3-030-26766-7_22
Siebers MSchmid USeuß DKunz MLautenbacher S(2016)Characterizing facial expressions by grammars of action unit sequences - A first investigation using ABLInformation Sciences: an International Journal10.1016/j.ins.2015.10.007329:C(866-875)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.ins.2015.10.007

Index Terms

Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 12, Issue

2/1/2011

3426 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

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Published: 01 July 2011

Published in JMLR Volume 12

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Yang RWang YWang BGong L(2019)Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical RecordsIntelligent Computing Methodologies10.1007/978-3-030-26766-7_22(235-243)Online publication date: 3-Aug-2019
https://dl.acm.org/doi/10.1007/978-3-030-26766-7_22
Siebers MSchmid USeuß DKunz MLautenbacher S(2016)Characterizing facial expressions by grammars of action unit sequences - A first investigation using ABLInformation Sciences: an International Journal10.1016/j.ins.2015.10.007329:C(866-875)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.ins.2015.10.007

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Abstract

References

Cited By

Index Terms

Recommendations

Nez: practical open grammar language

Natural language grammar induction with a generative constituent-context model

Tree insertion grammar: a cubic-time, parsable formalism that lexicalizes context-free grammar without changing the trees produced

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