Paper: A Fast and Accurate Dependency Parser using Neural Networks

ACL ID D14-1082
Title A Fast and Accurate Dependency Parser using Neural Networks
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Almost all current dependency parsers classify based on millions of sparse indi- cator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed signif- icantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based depen- dency parser. Because this classifier learns and uses just a small number of dense fea- tures, it can work very fast, while achiev- ing an about 2% improvement in unla- beled and labeled attachment scores on both English and Chinese datasets. Con- cretely, our parser is able to parse more than 1000 sentences per second at 92.2% unlabeled attachment score on the English Penn Treebank.