Making Dependency Labeling Simple, Fast and Accurate
This work addresses the task of dependency labeling assigning labels to an (unlabeled) dependency tree. We employ and extend a feature representation learning approach, optimizing it for both high speed and accuracy. We apply our labeling model on top of state of the art parsers and evaluate its performance on standard benchmarks including the CoNLL 2009 and the English PTB datasets. Our model processes over 1,700 English sentences per second, which is 30 times faster than the sparse feature method. It improves labeling accuracy over the outputs of top parsers, achieving the best LAS on 5 out of 7 datasets1.
NAACL