Abstract
Previous work has demonstrated the viability of a particular neural network architecture, Simple Synchrony Networks, for syntactic parsing. Here we present additional results on the performance of this type of parser, including direct comparisons on the same dataset with a standard statistical parsing method, Probabilistic Context Free Grammars. We focus these experiments on demonstrating one of the main advantages of the SSN parser over the PCFG, handling sparse data. We use smaller datasets than are typically used with statistical methods, resulting in the PCFG finding parses for under half of the test sentences, while the SSN finds parses for all sentences. Even on the PCFG ‘s parsed half, the SSN performs better than the PCFG, as measure by recall and precision on both constituents and a dependency-like measure.- Anthology ID:
- 2000.iwpt-1.14
- Volume:
- Proceedings of the Sixth International Workshop on Parsing Technologies
- Month:
- February 23-25
- Year:
- 2000
- Address:
- Trento, Italy
- Editors:
- Alberto Lavelli, John Carroll, Robert C. Berwick, Harry C. Bunt, Bob Carpenter, John Carroll, Ken Church, Mark Johnson, Aravind Joshi, Ronald Kaplan, Martin Kay, Bernard Lang, Alon Lavie, Anton Nijholt, Christer Samuelsson, Mark Steedman, Oliviero Stock, Hozumi Tanaka, Masaru Tomita, Hans Uszkoreit, K. Vijay-Shanker, David Weir, Mats Wiren
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 123–134
- Language:
- URL:
- https://aclanthology.org/2000.iwpt-1.14
- DOI:
- Bibkey:
- Cite (ACL):
- James Henderson. 2000. A Neural Network Parser that Handles Sparse Data. In Proceedings of the Sixth International Workshop on Parsing Technologies, pages 123–134, Trento, Italy. Association for Computational Linguistics.
- Cite (Informal):
- A Neural Network Parser that Handles Sparse Data (Henderson, IWPT 2000)
- Copy Citation:
- PDF:
- https://aclanthology.org/2000.iwpt-1.14.pdf
Export citation
@inproceedings{henderson-2000-neural,
title = "A Neural Network Parser that Handles Sparse Data",
author = "Henderson, James",
editor = "Lavelli, Alberto and
Carroll, John and
Berwick, Robert C. and
Bunt, Harry C. and
Carpenter, Bob and
Carroll, John and
Church, Ken and
Johnson, Mark and
Joshi, Aravind and
Kaplan, Ronald and
Kay, Martin and
Lang, Bernard and
Lavie, Alon and
Nijholt, Anton and
Samuelsson, Christer and
Steedman, Mark and
Stock, Oliviero and
Tanaka, Hozumi and
Tomita, Masaru and
Uszkoreit, Hans and
Vijay-Shanker, K. and
Weir, David and
Wiren, Mats",
booktitle = "Proceedings of the Sixth International Workshop on Parsing Technologies",
month = feb # " 23-25",
year = "2000",
address = "Trento, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2000.iwpt-1.14",
pages = "123--134",
abstract = "Previous work has demonstrated the viability of a particular neural network architecture, Simple Synchrony Networks, for syntactic parsing. Here we present additional results on the performance of this type of parser, including direct comparisons on the same dataset with a standard statistical parsing method, Probabilistic Context Free Grammars. We focus these experiments on demonstrating one of the main advantages of the SSN parser over the PCFG, handling sparse data. We use smaller datasets than are typically used with statistical methods, resulting in the PCFG finding parses for under half of the test sentences, while the SSN finds parses for all sentences. Even on the PCFG {`}s parsed half, the SSN performs better than the PCFG, as measure by recall and precision on both constituents and a dependency-like measure.",
}
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%0 Conference Proceedings %T A Neural Network Parser that Handles Sparse Data %A Henderson, James %Y Lavelli, Alberto %Y Carroll, John %Y Berwick, Robert C. %Y Bunt, Harry C. %Y Carpenter, Bob %Y Church, Ken %Y Johnson, Mark %Y Joshi, Aravind %Y Kaplan, Ronald %Y Kay, Martin %Y Lang, Bernard %Y Lavie, Alon %Y Nijholt, Anton %Y Samuelsson, Christer %Y Steedman, Mark %Y Stock, Oliviero %Y Tanaka, Hozumi %Y Tomita, Masaru %Y Uszkoreit, Hans %Y Vijay-Shanker, K. %Y Weir, David %Y Wiren, Mats %S Proceedings of the Sixth International Workshop on Parsing Technologies %D 2000 %8 feb 23 25 %I Association for Computational Linguistics %C Trento, Italy %F henderson-2000-neural %X Previous work has demonstrated the viability of a particular neural network architecture, Simple Synchrony Networks, for syntactic parsing. Here we present additional results on the performance of this type of parser, including direct comparisons on the same dataset with a standard statistical parsing method, Probabilistic Context Free Grammars. We focus these experiments on demonstrating one of the main advantages of the SSN parser over the PCFG, handling sparse data. We use smaller datasets than are typically used with statistical methods, resulting in the PCFG finding parses for under half of the test sentences, while the SSN finds parses for all sentences. Even on the PCFG ‘s parsed half, the SSN performs better than the PCFG, as measure by recall and precision on both constituents and a dependency-like measure. %U https://aclanthology.org/2000.iwpt-1.14 %P 123-134
Markdown (Informal)
[A Neural Network Parser that Handles Sparse Data](https://aclanthology.org/2000.iwpt-1.14) (Henderson, IWPT 2000)
- A Neural Network Parser that Handles Sparse Data (Henderson, IWPT 2000)
ACL
- James Henderson. 2000. A Neural Network Parser that Handles Sparse Data. In Proceedings of the Sixth International Workshop on Parsing Technologies, pages 123–134, Trento, Italy. Association for Computational Linguistics.