A Fast and Accurate Dependency Parser using Neural Networks
Danqi Chen, Christopher D. Manning · 2014
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. 1