Estimators for Stochastic "Unification-Based'' Grammars

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

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

3 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 languageEnglish
Title of host publicationProceedings of the 37th Annual Conference of the Association for Computational Linguistics
Place of PublicationSan Francisco
PublisherAssociation for Computational Linguistics (ACL)
Pages535-541
Number of pages7
ISBN (Print)1-55860-609-2
DOIs
Publication statusPublished - 1999
Externally publishedYes
EventAnnual Meeting of the Association for Computational Linguistics (37th : 1999) - University of Maryland, College Park, United States
Duration: 20 Jun 199926 Jun 1999

Conference

ConferenceAnnual Meeting of the Association for Computational Linguistics (37th : 1999)
CountryUnited States
CityCollege Park
Period20/06/9926/06/99

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.

Fingerprint Dive into the research topics of 'Estimators for Stochastic "Unification-Based'' Grammars'. Together they form a unique fingerprint.

Cite this