Efficient, Feature-based, Conditional Random Field Parsing

Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by gen- erative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artificially short sen- tences, we present the first general, feature- rich discriminative parser, based on a condi- tional 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 gener- ative baseline.