Figures of Merit for Best-First Probabilistic Chart Parsing (1996)
Cached
Download Links
- [acl.ldc.upenn.edu]
- [www.cs.brown.edu]
- [www.aclweb.org]
- [www.cs.brown.edu]
- [wilma.cs.brown.edu]
- [www.cs.brown.edu]
- [www.cs.brown.edu]
- [acl.ldc.upenn.edu]
- DBLP
Other Repositories/Bibliography
| Venue: | Computational Linguistics |
| Citations: | 65 - 3 self |
BibTeX
@ARTICLE{Caraballo96figuresof,
author = {Sharon A. Caraballo and Eugene Charniak},
title = {Figures of Merit for Best-First Probabilistic Chart Parsing},
journal = {Computational Linguistics},
year = {1996},
volume = {24},
pages = {275--298}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Best-first parsing methods for natural language try to parse efficiently by considering the most likely constituents first. Some figure of merit is needed by which to compare the likelihood of constituents, and the choice of this figure has a substantial impact on the efficiency of the parser. While several parsers described in the literature have used such techniques, there is no published data on their efficacy, much less attempts to judge their relative merits. We propose and evaluate several figures of merit for best-first parsing.
