Probabilistic Parsing using Left Corner Language Models

Christopher D. Manning, Bob Carpenter


Abstract
We introduce a novel parser based on a probabilistic version of a left-corner parser. The left-corner strategy is attractive because rule probabilities can be conditioned on both top-down goals and bottom-up derivations. We develop the underlying theory and explain how a grammar can be induced from analyzed data. We show that the left-corner approach provides an advantage over simple top-down probabilistic context-free grammars in parsing the Wall Street Journal using a grammar induced from the Penn Treebank. We also conclude that the Penn Treebank provides a fairly weak tes bed due to the flatness of its bracketings and to the obvious overgeneration and undergeneration of its induced grammar.
Anthology ID:
1997.iwpt-1.18
Volume:
Proceedings of the Fifth International Workshop on Parsing Technologies
Month:
September 17-20
Year:
1997
Address:
Boston/Cambridge, Massachusetts, USA
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
147–158
Language:
URL:
https://aclanthology.org/1997.iwpt-1.18
DOI:
Bibkey:
Cite (ACL):
Christopher D. Manning and Bob Carpenter. 1997. Probabilistic Parsing using Left Corner Language Models. In Proceedings of the Fifth International Workshop on Parsing Technologies, pages 147–158, Boston/Cambridge, Massachusetts, USA. Association for Computational Linguistics.
Cite (Informal):
Probabilistic Parsing using Left Corner Language Models (Manning & Carpenter, IWPT 1997)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/1997.iwpt-1.18.pdf