Abstract
We present a new formalism, probabilistic feature grammar (PFG). PFGs combine most of the best properties of several other formalisms, including those of Collins, Magerman, and Charniak, and in experiments have comparable or better performance. PFGs generate features one at a time, probabilistically, conditioning the probabilities of each feature on other features in a local context. Because the conditioning is local, efficient polynomial time parsing algorithms exist for computing inside, outside, and Viterbi parses. PFGs can produce probabilities of strings, making them potentially useful for language modeling. Precision and recall results are comparable to the state of the art with words, and the best reported without words.- Anthology ID:
- 1997.iwpt-1.13
- Volume:
- Proceedings of the Fifth International Workshop on Parsing Technologies
- Month:
- September 17-20
- Year:
- 1997
- Address:
- Boston/Cambridge, Massachusetts, USA
- Editors:
- Anton Nijholt, Robert C. Berwick, Harry C. Bunt, Bob Carpenter, Eva Hajicova, Mark Johnson, Aravind Joshi, Ronald Kaplan, Martin Kay, Bernard Lang, Alon Lavie, Makoto Nagao, Mark Steedman, Masaru Tomita, K. Vijay-Shanker, David Weir, Kent Wittenburg, Mats Wiren
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–100
- Language:
- URL:
- https://aclanthology.org/1997.iwpt-1.13
- DOI:
- Bibkey:
- Cite (ACL):
- Joshua Goodman. 1997. Probabilistic Feature Grammars. In Proceedings of the Fifth International Workshop on Parsing Technologies, pages 89–100, Boston/Cambridge, Massachusetts, USA. Association for Computational Linguistics.
- Cite (Informal):
- Probabilistic Feature Grammars (Goodman, IWPT 1997)
- Copy Citation:
- PDF:
- https://aclanthology.org/1997.iwpt-1.13.pdf
Export citation
@inproceedings{goodman-1997-probabilistic,
title = "Probabilistic Feature Grammars",
author = "Goodman, Joshua",
editor = "Nijholt, Anton and
Berwick, Robert C. and
Bunt, Harry C. and
Carpenter, Bob and
Hajicova, Eva and
Johnson, Mark and
Joshi, Aravind and
Kaplan, Ronald and
Kay, Martin and
Lang, Bernard and
Lavie, Alon and
Nagao, Makoto and
Steedman, Mark and
Tomita, Masaru and
Vijay-Shanker, K. and
Weir, David and
Wittenburg, Kent and
Wiren, Mats",
booktitle = "Proceedings of the Fifth International Workshop on Parsing Technologies",
month = sep # " 17-20",
year = "1997",
address = "Boston/Cambridge, Massachusetts, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/1997.iwpt-1.13",
pages = "89--100",
abstract = "We present a new formalism, probabilistic feature grammar (PFG). PFGs combine most of the best properties of several other formalisms, including those of Collins, Magerman, and Charniak, and in experiments have comparable or better performance. PFGs generate features one at a time, probabilistically, conditioning the probabilities of each feature on other features in a local context. Because the conditioning is local, efficient polynomial time parsing algorithms exist for computing inside, outside, and Viterbi parses. PFGs can produce probabilities of strings, making them potentially useful for language modeling. Precision and recall results are comparable to the state of the art with words, and the best reported without words.",
}
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%0 Conference Proceedings %T Probabilistic Feature Grammars %A Goodman, Joshua %Y Nijholt, Anton %Y Berwick, Robert C. %Y Bunt, Harry C. %Y Carpenter, Bob %Y Hajicova, Eva %Y Johnson, Mark %Y Joshi, Aravind %Y Kaplan, Ronald %Y Kay, Martin %Y Lang, Bernard %Y Lavie, Alon %Y Nagao, Makoto %Y Steedman, Mark %Y Tomita, Masaru %Y Vijay-Shanker, K. %Y Weir, David %Y Wittenburg, Kent %Y Wiren, Mats %S Proceedings of the Fifth International Workshop on Parsing Technologies %D 1997 %8 sep 17 20 %I Association for Computational Linguistics %C Boston/Cambridge, Massachusetts, USA %F goodman-1997-probabilistic %X We present a new formalism, probabilistic feature grammar (PFG). PFGs combine most of the best properties of several other formalisms, including those of Collins, Magerman, and Charniak, and in experiments have comparable or better performance. PFGs generate features one at a time, probabilistically, conditioning the probabilities of each feature on other features in a local context. Because the conditioning is local, efficient polynomial time parsing algorithms exist for computing inside, outside, and Viterbi parses. PFGs can produce probabilities of strings, making them potentially useful for language modeling. Precision and recall results are comparable to the state of the art with words, and the best reported without words. %U https://aclanthology.org/1997.iwpt-1.13 %P 89-100
Markdown (Informal)
[Probabilistic Feature Grammars](https://aclanthology.org/1997.iwpt-1.13) (Goodman, IWPT 1997)
- Probabilistic Feature Grammars (Goodman, IWPT 1997)
ACL
- Joshua Goodman. 1997. Probabilistic Feature Grammars. In Proceedings of the Fifth International Workshop on Parsing Technologies, pages 89–100, Boston/Cambridge, Massachusetts, USA. Association for Computational Linguistics.