PCFG learning by nonterminal partition search

Anja Belz

doi:10.1.1.12.387

PCFG learning by nonterminal partition search

Anja Belz

University of Brighton

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

pcfg Learning by Partition Search is a general grammatical inference method for constructing, adapting and optimising pcfgs. Given a training corpus of examples from a language, a canonical grammar for the training corpus, and a parsing task, Partition Search pcfg Learning constructs a grammar that maximises performance on the parsing task and minimises grammar size. This paper describes Partition Search in detail, also providing theoretical background and a characterisation of the family of inference methods it belongs to. The paper also reports an example application to the task of building grammars for noun phrase extraction, a task that is crucial in many applications involving natu- ral language processing. In the experiments, Partition Search improves parsing performance by up to 21.45% compared to a general baseline and by up to 3.48% compared to a task-specific baseline, while reducing grammar size by up to 17.25%.

Original language	English
Title of host publication	Grammatical Inference: Algorithms and Applications: Proceedings of the 6th International Colloquium: ICGI 2002
Editors	P. Adriaans, H. Fernau, M. van Zaanen
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	14-27
Number of pages	14
Volume	2484/2
ISBN (Electronic)	1611-3349
ISBN (Print)	0302-9743
DOIs	https://doi.org/10.1.1.12.387
Publication status	Published - 1 Jan 2002
Event	Grammatical Inference: Algorithms and Applications: Proceedings of the 6th International Colloquium: ICGI 2002 - Amsterdam, The Netherlands, September 23-25, 2002 Duration: 1 Jan 2002 → …

Publication series

Name	Lecture notes in computer science

Conference

Conference	Grammatical Inference: Algorithms and Applications: Proceedings of the 6th International Colloquium: ICGI 2002
Period	1/01/02 → …

Keywords

Partition search

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Cite this

@inproceedings{b83c41ef3b5d4425bff235f78fd7a41c,

title = "PCFG learning by nonterminal partition search",

abstract = "pcfg Learning by Partition Search is a general grammatical inference method for constructing, adapting and optimising pcfgs. Given a training corpus of examples from a language, a canonical grammar for the training corpus, and a parsing task, Partition Search pcfg Learning constructs a grammar that maximises performance on the parsing task and minimises grammar size. This paper describes Partition Search in detail, also providing theoretical background and a characterisation of the family of inference methods it belongs to. The paper also reports an example application to the task of building grammars for noun phrase extraction, a task that is crucial in many applications involving natu- ral language processing. In the experiments, Partition Search improves parsing performance by up to 21.45% compared to a general baseline and by up to 3.48% compared to a task-specific baseline, while reducing grammar size by up to 17.25%.",

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author = "Anja Belz",

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booktitle = "Grammatical Inference: Algorithms and Applications: Proceedings of the 6th International Colloquium: ICGI 2002",

note = "Grammatical Inference: Algorithms and Applications: Proceedings of the 6th International Colloquium: ICGI 2002 ; Conference date: 01-01-2002",

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Belz, A 2002, PCFG learning by nonterminal partition search. in P Adriaans, H Fernau & M van Zaanen (eds), Grammatical Inference: Algorithms and Applications: Proceedings of the 6th International Colloquium: ICGI 2002. vol. 2484/2, Lecture notes in computer science, Springer, Berlin, Germany, pp. 14-27, Grammatical Inference: Algorithms and Applications: Proceedings of the 6th International Colloquium: ICGI 2002, 1/01/02. https://doi.org/10.1.1.12.387

PCFG learning by nonterminal partition search. / Belz, Anja.
Grammatical Inference: Algorithms and Applications: Proceedings of the 6th International Colloquium: ICGI 2002. ed. / P. Adriaans; H. Fernau; M. van Zaanen. Vol. 2484/2 Berlin, Germany: Springer, 2002. p. 14-27 (Lecture notes in computer science).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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AB - pcfg Learning by Partition Search is a general grammatical inference method for constructing, adapting and optimising pcfgs. Given a training corpus of examples from a language, a canonical grammar for the training corpus, and a parsing task, Partition Search pcfg Learning constructs a grammar that maximises performance on the parsing task and minimises grammar size. This paper describes Partition Search in detail, also providing theoretical background and a characterisation of the family of inference methods it belongs to. The paper also reports an example application to the task of building grammars for noun phrase extraction, a task that is crucial in many applications involving natu- ral language processing. In the experiments, Partition Search improves parsing performance by up to 21.45% compared to a general baseline and by up to 3.48% compared to a task-specific baseline, while reducing grammar size by up to 17.25%.

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PCFG learning by nonterminal partition search

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