Making dependency labeling simple, fast and accurate

Abstract

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.