Estimators for Stochastic "Unification-Based'' Grammars

Mark Johnson; Stuart Geman; Stephen Canon; Zhiyi Chi; Stefan Riezler

doi:10.3115/1034678.1034758

Estimators for Stochastic "Unification-Based'' Grammars

Mark Johnson, Stuart Geman, Stephen Canon, Zhiyi Chi, Stefan Riezler

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

117 Citations (Scopus)

19 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the 37th Annual Conference of the Association for Computational Linguistics
Place of Publication	San Francisco
Publisher	Association for Computational Linguistics (ACL)
Pages	535-541
Number of pages	7
ISBN (Print)	1-55860-609-2
DOIs	https://doi.org/10.3115/1034678.1034758
Publication status	Published - 1999
Externally published	Yes
Event	Annual Meeting of the Association for Computational Linguistics (37th : 1999) - University of Maryland, College Park, United States Duration: 20 Jun 1999 → 26 Jun 1999

Conference

Conference	Annual Meeting of the Association for Computational Linguistics (37th : 1999)
Country/Territory	United States
City	College Park
Period	20/06/99 → 26/06/99

Bibliographical note

Copyright the Publisher 1999. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Access to Document

10.3115/1034678.1034758

Publisher version (open access)Final published version, 638 KBLicence: CC BY-NC-SA

Cite this

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title = "Estimators for Stochastic {"}Unification-Based'' Grammars",

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.",

author = "Mark Johnson and Stuart Geman and Stephen Canon and Zhiyi Chi and Stefan Riezler",

note = "Copyright the Publisher 1999. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. ; Annual Meeting of the Association for Computational Linguistics (37th : 1999) ; Conference date: 20-06-1999 Through 26-06-1999",

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doi = "10.3115/1034678.1034758",

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Johnson, M, Geman, S, Canon, S, Chi, Z & Riezler, S 1999, Estimators for Stochastic "Unification-Based'' Grammars. in Proceedings of the 37th Annual Conference of the Association for Computational Linguistics. Association for Computational Linguistics (ACL), San Francisco, pp. 535-541, Annual Meeting of the Association for Computational Linguistics (37th : 1999), College Park, Maryland, United States, 20/06/99. https://doi.org/10.3115/1034678.1034758

Estimators for Stochastic "Unification-Based'' Grammars. / Johnson, Mark; Geman, Stuart; Canon, Stephen et al.
Proceedings of the 37th Annual Conference of the Association for Computational Linguistics. San Francisco: Association for Computational Linguistics (ACL), 1999. p. 535-541.

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

TY - GEN

T1 - Estimators for Stochastic "Unification-Based'' Grammars

AU - Johnson, Mark

AU - Geman, Stuart

AU - Canon, Stephen

AU - Chi, Zhiyi

AU - Riezler, Stefan

N1 - Copyright the Publisher 1999. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

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U2 - 10.3115/1034678.1034758

DO - 10.3115/1034678.1034758

M3 - Conference proceeding contribution

SN - 1-55860-609-2

SP - 535

EP - 541

BT - Proceedings of the 37th Annual Conference of the Association for Computational Linguistics

PB - Association for Computational Linguistics (ACL)

CY - San Francisco

T2 - Annual Meeting of the Association for Computational Linguistics (37th : 1999)

Y2 - 20 June 1999 through 26 June 1999

ER -

Estimators for Stochastic "Unification-Based'' Grammars

Abstract

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