Article

Free access

Alternative approaches for generating bodies of grammar rules

Authors:

Gabriel Infante-Lopez,

Maarten de RijkeAuthors Info & Claims

ACL '04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics

Pages 454 - es

https://doi.org/10.3115/1218955.1219013

Published: 21 July 2004 Publication History

Abstract

We compare two approaches for describing and generating bodies of rules used for natural language parsing. In today's parsers rule bodies do not exist a priori but are generated on the fly, usually with methods based on n-grams, which are one particular way of inducing probabilistic regular languages. We compare two approaches for inducing such languages. One is based on n-grams, the other on minimization of the Kullback-Leibler divergence. The inferred regular languages are used for generating bodies of rules inside a parsing procedure. We compare the two approaches along two dimensions: the quality of the probabilistic regular language they produce, and the performance of the parser they were used to build. The second approach outperforms the first one along both dimensions.

References

[1]

S. Abney, D. McAllester, and F. Pereira. 1999. Relating probabilistic grammars and automata. In Proc. 37th Annual Meeting of the ACL, pages 542--549.

Digital Library

[2]

T. Booth and R. Thompson. 1973. Applying probability measures to abstract languages. IEEE Transaction on Computers, C-33(5):442--450.

Digital Library

[3]

R. Carrasco and J. Oncina. 1994. Learning stochastic regular grammars by means of state merging method. In Proc. ICGI-94, Springer, pages 139--150.

Digital Library

[4]

E. Charniak. 1997. Statistical parsing with a context-free grammar and word statistics. In Proc. 14th Nat. Conf. on Artificial Intelligence, pages 598--603.

Digital Library

[5]

G. Chastellier and A. Colmerauer. 1969. W-grammar. In Proc. 1969 24th National Conf., pages 511--518.

Digital Library

[6]

M. Collins. 1996. A new statistical parser based on bigram lexical dependencies. In Proc. 34th Annual Meeting of the ACL, pages 184--191.

Digital Library

[7]

M. Collins. 1997. Three generative, lexicalized models for statistical parsing. In Proc. 35th Annual Meeting of the ACL and 8th Conf. of the EACL, pages 16--23.

Digital Library

[8]

M. Collins. 1999. Head-Driven Statistical Models for Natural Language Parsing. Ph.D. thesis, University of Pennsylvania, PA.

Digital Library

[9]

M. Collins. 2000. Discriminative reranking for natural language parsing. In Proc. ICML-2000, Stanford, Ca.

Digital Library

[10]

T. Cover and J. Thomas. 1991. Elements of Information Theory. Jonh Wiley and Sons, New York.

Digital Library

[11]

F. Denis. 2001. Learning regular languages from simple positive examples. Machine Learning, 44(1/2):37--66.

Digital Library

[12]

P. Dupont and L. Chase. 1998. Using symbol clustering to improve probabilistic automaton inference. In Proc. ICGI-98, pages 232--243.

Digital Library

[13]

J. Eisner. 1996. Three new probabilistic models for dependency parsing: An exploration. In Proc. COLING-96, pages 340--245, Copenhagen, Denmark.

Digital Library

[14]

J. Eisner. 2000. Bilexical grammars and their cubic-time parsing algorithms. In Advances in Probabilistic and Other Parsing Technologies, pages 29--62. Kluwer.

[15]

E. M. Gold. 1967. Language identification in the limit. Information and Control, 10:447--474.

[16]

G. Infante-Lopez and M. de Rijke. 2003. Natural language parsing with W-grammars. In Proc. CLIN 2003.

[17]

D. Klein and C. Manning. 2003. Accurate unlexicalized parsing. In Proc. 41st Annual Meeting of the ACL.

Digital Library

[18]

A. Krotov, M. Hepple, R. J. Gaizauskas, and Y. Wilks. 1998. Compacting the Penn Treebank grammar. In Proc. COLING-ACL, pages 699--703.

Digital Library

[19]

G. Kruijff. 2003. 3-phase grammar learning. In Proc. Workshop on Ideas and Strategies for Multilingual Grammar Development.

[20]

D. Lin. 1995. A dependency-based method for evaluating broad-coverage parsers. In Proc. IJCAI-95.

Digital Library

[21]

K. Sima'an. 2000. Tree-gram Parsing: Lexical Dependencies and Structural Relations. In Proc. 38th Annual Meeting of the ACL, pages 53--60, Hong Kong, China.

Digital Library

[22]

F. Thollard, P. Dupont, and C. de la Higuera. 2000. Probabilistic DFA inference using kullback-leibler divergence and minimality. In Proc. ICML 2000.

Digital Library

Cited By

Luque FQuattoni ABalle BCarreras XDaelemans W(2012)Spectral learning for non-deterministic dependency parsingProceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics10.5555/2380816.2380867(409-419)Online publication date: 23-Apr-2012
https://dl.acm.org/doi/10.5555/2380816.2380867
Damaševičius R(2010)Structural analysis of regulatory DNA sequences using grammar inference and Support Vector MachineNeurocomputing10.1016/j.neucom.2009.09.01873:4-6(633-638)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.neucom.2009.09.018
Infante-Lopez Gde Rijke M(2006)Sequences of part of speech tags vs. sequences of phrase labelsProceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing10.1007/11671299_20(180-191)Online publication date: 19-Feb-2006
https://dl.acm.org/doi/10.1007/11671299_20

Recommendations

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

Tree insertion grammar (TIG) is a tree-based formalism that makes use of tree substitution and tree adjunction. TIG is related to tree adjoining grammar. However, the adjunction permitted in TIG is sufficiently restricted that TIGs only derive context-...
Lexical and syntactic rules in a Tree Adjoining Grammar
ACL '90: Proceedings of the 28th annual meeting on Association for Computational Linguistics

Taking examples from English and French idioms, this paper shows that not only constituent structures rules but also most syntactic rules (such as topicalization, wh-question, pronominalization ...) are subject to lexical constraints (on top of ...
Tree insertion grammar: a cubic-time, parsable formalism that lexicalizes context-free grammar without changing the trees produced

Tree insertion grammar (TIG) is a tree-based formalism that makes use of tree substitution and tree adjunction. TIG is related to tree adjoining grammar. However, the adjunction permitted in TIG is sufficiently restricted that TIGs only derive context-...

Comments

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

ACL '04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics

July 2004

729 pages

General Chair:
Donia Scott
ITRI, University of Brighton

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 21 July 2004

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 85 of 443 submissions, 19%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
215
Total Downloads

Downloads (Last 12 months)52
Downloads (Last 6 weeks)3

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Luque FQuattoni ABalle BCarreras XDaelemans W(2012)Spectral learning for non-deterministic dependency parsingProceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics10.5555/2380816.2380867(409-419)Online publication date: 23-Apr-2012
https://dl.acm.org/doi/10.5555/2380816.2380867
Damaševičius R(2010)Structural analysis of regulatory DNA sequences using grammar inference and Support Vector MachineNeurocomputing10.1016/j.neucom.2009.09.01873:4-6(633-638)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.neucom.2009.09.018
Infante-Lopez Gde Rijke M(2006)Sequences of part of speech tags vs. sequences of phrase labelsProceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing10.1007/11671299_20(180-191)Online publication date: 19-Feb-2006
https://dl.acm.org/doi/10.1007/11671299_20

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

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

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten