Most existing graph-based parsing models rely on millions of hand-crafted features, which limits their generalization ability and slows down the parsing speed. In this paper, we propose a general and effective Neural Network model for graph-based dependency parsing. Our model can auto-matically learn high-order feature combi-nations using only atomic features by ex-ploiting a novel activation function tanh-cube. Moreover, we propose a simple yet effective way to utilize phrase-level infor-mation that is expensive to use in conven-tional graph-based parsers. Experiments on the English Penn Treebank show that parsers based on our model perform better than conventional graph-based parsers.
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