Article

Generating a grammar for statistical training

Authors:

R. A. Sharman,

F. Jelinek,

R. MercerAuthors Info & Claims

HLT '90: Proceedings of the workshop on Speech and Natural Language

Pages 267 - 274

https://doi.org/10.3115/116580.116667

Published: 24 June 1990 Publication History

Abstract

Parsing sentences of a Natural Language(NL) is an essential requirement for a variety of NL applications, and has been extensively studied. In particular, the sort of tasks which it would be desirable to do, include the ability to tag each word with its part-of-speech; to delineate with brackets, and label with a category name, each syntactic phrase; and to be able to adapt to different types of source material. Despite some 30 years of active research performing these tasks with a high degree of accuracy on unrestricted text is still an unsolved problem.

References

[1]

Baker, J. K. Trainable Grammars for Speech Recognition, in D. H. Klatt and J. J. Wolf (eds), Speech communication papers for the 97th meeting of the Acoustical Society of America, 1979

Google Scholar

[2]

Fujisaki, T A Probabilistic Modelling Methods for Language Parsing, IBM research Report RC 13296, Yorktown 1987

Google Scholar

[3]

Gazdar, G., E. Klein, G. K. Pullum and I. A. Sag Generalised Phrase Structure Grammar, Blackwell 1985

Google Scholar

[4]

Hopcroft, J. E. and J. D. Ullman Introduction to Automata, Theory, Languages, and computation, Addison-Wesley 1979

Digital Library

Google Scholar

[5]

Jelinek, F. The development of a Large Vocabulary Speech recognition system, Proc. IEEE vol. 73, No. 11, 1985(a)

Google Scholar

[6]

Jelinek, F. Markov Source Modelling of Text Generation Report of the Continuous Speech Recognition Group, IBM T. J. Watson Research Center, NY. 1985(b)

Google Scholar

[7]

Sharman, R. A. A Categorial Phrase Structure Grammar Development system, IBM UKSC Technical Report 198, 1989(a)

Google Scholar

[8]

Wetherill, C. S., Probabilistic Languages: A Review and some Open Questions, Computing Surveys, vol. 12, No. 4, 1980

Digital Library

Google Scholar

Cited By

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Hogenhout WMatsumoto Y(1998)A fast method for statistical grammar inductionNatural Language Engineering10.1017/S13513249980019834:3(191-209)Online publication date: 1-Sep-1998
https://dl.acm.org/doi/10.1017/S1351324998001983
Draggiotis AGrigoriadou MPhilokyprou G(1998)The DINOUS parserNatural Language Engineering10.1017/S13513249970018004:2(145-173)Online publication date: 1-Jun-1998
https://dl.acm.org/doi/10.1017/S1351324997001800
Black EEubank SKashioka HMagerman DGarside RLeech GTsujii J(1996)Beyond skeleton parsingProceedings of the 16th conference on Computational linguistics - Volume 110.3115/992628.992650(107-112)Online publication date: 5-Aug-1996
https://dl.acm.org/doi/10.3115/992628.992650
Show More Cited By

Index Terms

Generating a grammar for statistical training
1. Applied computing
  1. Arts and humanities
    1. Language translation
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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HLT '90: Proceedings of the workshop on Speech and Natural Language

June 1990

450 pages

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Association for Computational Linguistics

United States

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Published: 24 June 1990

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Overall Acceptance Rate 240 of 768 submissions, 31%

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Hogenhout WMatsumoto Y(1998)A fast method for statistical grammar inductionNatural Language Engineering10.1017/S13513249980019834:3(191-209)Online publication date: 1-Sep-1998
https://dl.acm.org/doi/10.1017/S1351324998001983
Draggiotis AGrigoriadou MPhilokyprou G(1998)The DINOUS parserNatural Language Engineering10.1017/S13513249970018004:2(145-173)Online publication date: 1-Jun-1998
https://dl.acm.org/doi/10.1017/S1351324997001800
Black EEubank SKashioka HMagerman DGarside RLeech GTsujii J(1996)Beyond skeleton parsingProceedings of the 16th conference on Computational linguistics - Volume 110.3115/992628.992650(107-112)Online publication date: 5-Aug-1996
https://dl.acm.org/doi/10.3115/992628.992650
Brill E(1995)Transformation-based error-driven learning and natural language processingComputational Linguistics10.5555/218355.21836721:4(543-565)Online publication date: 1-Dec-1995
https://dl.acm.org/doi/10.5555/218355.218367
Jelinek FLafferty JMagerman DMercer RRatnaparkhi ARoukos S(1994)Decision tree parsing using a hidden derivation modelProceedings of the workshop on Human Language Technology10.3115/1075812.1075873(272-277)Online publication date: 8-Mar-1994
https://dl.acm.org/doi/10.3115/1075812.1075873
Briscoe TCarroll J(1993)Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammarsComputational Linguistics10.5555/972450.97245319:1(25-59)Online publication date: 1-Mar-1993
https://dl.acm.org/doi/10.5555/972450.972453
Brill ESchubert L(1993)Automatic grammar induction and parsing free textProceedings of the 31st annual meeting on Association for Computational Linguistics10.3115/981574.981609(259-265)Online publication date: 22-Jun-1993
https://dl.acm.org/doi/10.3115/981574.981609
Black EJelinek FLafferty JMagerman DMercer RRoukos SSchubert L(1993)Towards history-based grammarsProceedings of the 31st annual meeting on Association for Computational Linguistics10.3115/981574.981579(31-37)Online publication date: 22-Jun-1993
https://dl.acm.org/doi/10.3115/981574.981579
Brill E(1993)Automatic grammar induction and parsing free textProceedings of the workshop on Human Language Technology10.3115/1075671.1075726(237-242)Online publication date: 21-Mar-1993
https://dl.acm.org/doi/10.3115/1075671.1075726
Jacobs P(1992)Parsing run amokProceedings of the tenth national conference on Artificial intelligence10.5555/1867135.1867184(315-321)Online publication date: 12-Jul-1992
https://dl.acm.org/doi/10.5555/1867135.1867184
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Recommendations

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

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

Generative models for statistical parsing with Combinatory Categorial Grammar

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