Article

Free access

Estimators for stochastic "Unification-Based" grammars

Authors:

Stefan RiezlerAuthors Info & Claims

ACL '99: Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics

Pages 535 - 541

https://doi.org/10.3115/1034678.1034758

Published: 20 June 1999 Publication History

PDF eReader

Abstract

Log-linear models provide a statistically sound framework for Stochastic "Unification-Based" Grammars (SUBGs) and stochastic versions of other kinds of grammars. We describe two computationally-tractable ways of estimating the parameters of such grammars from a training corpus of syntactic analyses, and apply these to estimate a stochastic version of Lexical-Functional Grammar.

References

[1]

Steven P. Abney. 1997. Stochastic Attribute-Value Grammars. Computational Linguistics, 23(4): 597--617.

Digital Library

Google Scholar

[2]

Adam L. Berger, Vincent J. Della Pietra, and Stephen A. Della Pietra. 1996. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1): 39--71.

Digital Library

Google Scholar

[3]

J. Besag. 1974. Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society, Series D, 36: 192--236.

Google Scholar

[4]

J. Besag. 1975. Statistical analysis of non-lattice data. The Statistician, 24: 179--195.

Crossref

Google Scholar

[5]

Zhiyi Chi. 1998. Probability Models for Complex Systems. Ph.D. thesis, Brown University.

Digital Library

Google Scholar

[6]

Brendan J. Frey. 1998. Graphical Models for Machine Learning and Digital Communication. The MIT Press, Cambridge, Massachusetts.

Digital Library

Google Scholar

[7]

Jerry R. Hobbs and John Bear. 1995. Two principles of parse preference. In Antonio Zampolli, Nicoletta Calzolari, and Martha Palmer, editors, Linguistica Computazionale: Current Issues in Computational Linguistics: In Honour of Don Walker, pages 503--512. Kluwer.

Digital Library

Google Scholar

[8]

Frederick Jelinek. 1997. Statistical Methods for Speech Recognition. The MIT Press, Cambridge, Massachusetts.

Digital Library

Google Scholar

[9]

John T. Maxwell III and Ronald M. Kaplan. 1995. A method for disjunctive constraint satisfaction. In Mary Dalrymple, Ronald M. Kaplan, John T. Maxwell III, and Annie Zaenen, editors, Formal Issues in Lexical-Functional Grammar, number 47 in CSLI Lecture Notes Series, chapter 14, pages 381--481. CSLI Publications.

Google Scholar

[10]

Judea Pearl. 1988. Probabalistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, California.

Digital Library

Google Scholar

[11]

William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. 1992. Numerical Recipies in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, England, 2nd edition.

Digital Library

Google Scholar

Cited By

View all

Corazza ALavelli ASatta G(2013)An information-theoretic measure to evaluate parsing difficulty across treebanksACM Transactions on Speech and Language Processing 10.1145/2407736.24077379:4(1-31)Online publication date: 30-Jan-2013
https://dl.acm.org/doi/10.1145/2407736.2407737
Wong SDras MMerlo PBarzilay RJohnson M(2011)Exploiting parse structures for native language identificationProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/2145432.2145603(1600-1610)Online publication date: 27-Jul-2011
https://dl.acm.org/doi/10.5555/2145432.2145603
Artzi YZettlemoyer LMerlo PBarzilay RJohnson M(2011)Bootstrapping semantic parsers from conversationsProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/2145432.2145481(421-432)Online publication date: 27-Jul-2011
https://dl.acm.org/doi/10.5555/2145432.2145481
Show More Cited By

Estimators for stochastic "Unification-Based" grammars
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

Recommendations

Highly Constrained Unification Grammars

Unification grammars are widely accepted as an expressive means for describing the structure of natural languages. In general, the recognition problem is undecidable for unification grammars. Even with restricted variants of the formalism, off-line ...
Highly constrained unification grammars
ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics

Unification grammars are widely accepted as an expressive means for describing the structure of natural languages. In general, the recognition problem is undecidable for unification grammars. Even with restricted variants of the formalism, offline ...
Exploiting auxiliary distributions in stochastic unification-based grammars
NAACL 2000: Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference

This paper describes a method for estimating conditional probability distributions over the parses of "unification-based" grammars which can utilize auxiliary distributions that are estimated by other means. We show how this can be used to incorporate ...

Comments

Information & Contributors

Information

Published In

ACL '99: Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics

June 1999

642 pages

ISBN:1558606093

Program Chairs:
Robert Dale,
Ken Church

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 20 June 1999

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 85 of 443 submissions, 19%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

66
Total Citations
View Citations
325
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)2

Reflects downloads up to 27 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Corazza ALavelli ASatta G(2013)An information-theoretic measure to evaluate parsing difficulty across treebanksACM Transactions on Speech and Language Processing 10.1145/2407736.24077379:4(1-31)Online publication date: 30-Jan-2013
https://dl.acm.org/doi/10.1145/2407736.2407737
Wong SDras MMerlo PBarzilay RJohnson M(2011)Exploiting parse structures for native language identificationProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/2145432.2145603(1600-1610)Online publication date: 27-Jul-2011
https://dl.acm.org/doi/10.5555/2145432.2145603
Artzi YZettlemoyer LMerlo PBarzilay RJohnson M(2011)Bootstrapping semantic parsers from conversationsProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/2145432.2145481(421-432)Online publication date: 27-Jul-2011
https://dl.acm.org/doi/10.5555/2145432.2145481
de Kok DPlank Bvan Noord GLin D(2011)Reversible stochastic attribute-value grammarsProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 210.5555/2002736.2002778(194-199)Online publication date: 19-Jun-2011
https://dl.acm.org/doi/10.5555/2002736.2002778
Zhang YBaldwin TKordoni VMartinez DNicholson JKaplan R(2010)Chart mining-based lexical acquisition with precision grammarsHuman Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics10.5555/1857999.1858001(10-18)Online publication date: 2-Jun-2010
https://dl.acm.org/doi/10.5555/1857999.1858001
Merity SMurphy TCurran JKan MTeufel S(2009)Accurate argumentative zoning with maximum entropy modelsProceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries10.5555/1699750.1699754(19-26)Online publication date: 7-Aug-2009
https://dl.acm.org/doi/10.5555/1699750.1699754
Chowdhury MZhang YKordoni VBunt H(2009)Using treebanking discriminants as parse disambiguation featuresProceedings of the 11th International Conference on Parsing Technologies10.5555/1697236.1697282(226-229)Online publication date: 7-Oct-2009
https://dl.acm.org/doi/10.5555/1697236.1697282
Zettlemoyer LCollins MSu K(2009)Learning context-dependent mappings from sentences to logical formProceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 210.5555/1690219.1690283(976-984)Online publication date: 2-Aug-2009
https://dl.acm.org/doi/10.5555/1690219.1690283
Johnson MBaldwin TKordoni V(2009)How the statistical revolution changes (computational) linguisticsProceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics: Virtuous, Vicious or Vacuous?10.5555/1642038.1642041(3-11)Online publication date: 30-Mar-2009
https://dl.acm.org/doi/10.5555/1642038.1642041
Plank BDemberg VMa YReiter N(2009)Structural correspondence learning for parse disambiguationProceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop10.5555/1609179.1609184(37-45)Online publication date: 2-Apr-2009
https://dl.acm.org/doi/10.5555/1609179.1609184
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Recommendations

Highly Constrained Unification Grammars

Highly constrained unification grammars

Exploiting auxiliary distributions in stochastic unification-based grammars

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations