A New Formalization of Probabilistic GLR Parsing

Kentaro Unui, Virach Sornlertlamvanich, Hozumi Tanaka, Takenobu Tokunaga


Abstract
This paper presents a new formalization of probabilistic GLR language modeling for statistical parsing. Our model inherits its essential features from Briscoe and Carroll’s generalized probabilistic LR model, which obtains context-sensitivity by assigning a probability to each LR parsing action according to its left and right context. Briscoe and Carroll’s model, however, has a drawback in that it is not formalized in any probabilistically well-founded way, which may degrade its parsing performance. Our formulation overcomes this drawback with a few significant refinements, while maintaining all the advantages of Briscoe and Carroll’s modeling.
Anthology ID:
1997.iwpt-1.16
Volume:
Proceedings of the Fifth International Workshop on Parsing Technologies
Month:
September 17-20
Year:
1997
Address:
Boston/Cambridge, Massachusetts, USA
Venue:
IWPT
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
123–134
Language:
URL:
https://aclanthology.org/1997.iwpt-1.16
DOI:
Bibkey:
Cite (ACL):
Kentaro Unui, Virach Sornlertlamvanich, Hozumi Tanaka, and Takenobu Tokunaga. 1997. A New Formalization of Probabilistic GLR Parsing. In Proceedings of the Fifth International Workshop on Parsing Technologies, pages 123–134, Boston/Cambridge, Massachusetts, USA. Association for Computational Linguistics.
Cite (Informal):
A New Formalization of Probabilistic GLR Parsing (Unui et al., IWPT 1997)
Copy Citation:
PDF:
https://aclanthology.org/1997.iwpt-1.16.pdf