Parsing with lexicalized probabilistic recursive transition networks

Alexis Nasr; Owen Rambow

doi:10.1007/11780885_16

Parsing with lexicalized probabilistic recursive transition networks

Alexis Nasr
, Owen Rambow

Linguistics

Université Paris 7

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

1 Scopus citations

Abstract

We present a formalization of lexicalized Recursive Transition Networks which we call Automaton-Based Generative Dependency Grammar (gdg). We show how to extract a gdg from a syntactically annotated corpus, present a chart parser for gdg, and discuss different probabilistic models which are directly implemented in the finite automata and do not affect the parser.

Original language	English
Title of host publication	Finite-State Methods and Natural Language Processing - 5th InternationalWorkshop, FSMNLP 2005, Revised Papers
Editors	Anssi Yli-Jyra, Lauri Karttunen, Juhani Karhumaki
Publisher	Springer Verlag
Pages	156-166
Number of pages	11
ISBN (Print)	9783540354673
DOIs	https://doi.org/10.1007/11780885_16
State	Published - 2006
Event	5th International Workshop of Finite-State Methods and Natural Language Processing, FSMNLP 2005 - Helsinki, Finland Duration: Sep 1 2005 → Sep 2 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4002 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Workshop of Finite-State Methods and Natural Language Processing, FSMNLP 2005
Country/Territory	Finland
City	Helsinki
Period	09/1/05 → 09/2/05

Access to Document

10.1007/11780885_16

Cite this

Nasr, A., & Rambow, O. (2006). Parsing with lexicalized probabilistic recursive transition networks. In A. Yli-Jyra, L. Karttunen, & J. Karhumaki (Eds.), Finite-State Methods and Natural Language Processing - 5th InternationalWorkshop, FSMNLP 2005, Revised Papers (pp. 156-166). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4002 LNAI). Springer Verlag. https://doi.org/10.1007/11780885_16

Nasr, Alexis ; Rambow, Owen. / Parsing with lexicalized probabilistic recursive transition networks. Finite-State Methods and Natural Language Processing - 5th InternationalWorkshop, FSMNLP 2005, Revised Papers. editor / Anssi Yli-Jyra ; Lauri Karttunen ; Juhani Karhumaki. Springer Verlag, 2006. pp. 156-166 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We present a formalization of lexicalized Recursive Transition Networks which we call Automaton-Based Generative Dependency Grammar (gdg). We show how to extract a gdg from a syntactically annotated corpus, present a chart parser for gdg, and discuss different probabilistic models which are directly implemented in the finite automata and do not affect the parser.",

author = "Alexis Nasr and Owen Rambow",

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Nasr, A & Rambow, O 2006, Parsing with lexicalized probabilistic recursive transition networks. in A Yli-Jyra, L Karttunen & J Karhumaki (eds), Finite-State Methods and Natural Language Processing - 5th InternationalWorkshop, FSMNLP 2005, Revised Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4002 LNAI, Springer Verlag, pp. 156-166, 5th International Workshop of Finite-State Methods and Natural Language Processing, FSMNLP 2005, Helsinki, Finland, 09/1/05. https://doi.org/10.1007/11780885_16

Parsing with lexicalized probabilistic recursive transition networks. / Nasr, Alexis; Rambow, Owen.
Finite-State Methods and Natural Language Processing - 5th InternationalWorkshop, FSMNLP 2005, Revised Papers. ed. / Anssi Yli-Jyra; Lauri Karttunen; Juhani Karhumaki. Springer Verlag, 2006. p. 156-166 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4002 LNAI).

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

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AB - We present a formalization of lexicalized Recursive Transition Networks which we call Automaton-Based Generative Dependency Grammar (gdg). We show how to extract a gdg from a syntactically annotated corpus, present a chart parser for gdg, and discuss different probabilistic models which are directly implemented in the finite automata and do not affect the parser.

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Nasr A, Rambow O. Parsing with lexicalized probabilistic recursive transition networks. In Yli-Jyra A, Karttunen L, Karhumaki J, editors, Finite-State Methods and Natural Language Processing - 5th InternationalWorkshop, FSMNLP 2005, Revised Papers. Springer Verlag. 2006. p. 156-166. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11780885_16

Parsing with lexicalized probabilistic recursive transition networks

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