Efficient, Feature-based, Conditional Random Field Parsing

Abstract

Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data. Our efficiency is primarily due to the use of stochastic optimization techniques, as well as parallelization and chart prefiltering. On WSJ15, we attain a state-of-the-art F-score of 90.9%, a 14% relative reduction in error over previous models, while being two orders of magnitude faster. On sentences of length 40, our system achieves an F-score of 89.0%, a 36% relative reduction in error over a generative baseline.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 74,310

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

  • Only published works are available at libraries.

Similar books and articles

Operating on Functions with Variable Domains.Philip G. Calabrese - 2003 - Journal of Philosophical Logic 32 (1):1-18.

Analytics

Added to PP
2010-12-22

Downloads
76 (#157,211)

6 months
1 (#415,900)

Historical graph of downloads
How can I increase my downloads?