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 automatically learn high-order feature combinations using only atomic features by exploiting a novel activation function tanhcube. Moreover, we propose a simple yet effective way to utilize phrase-level information that is expensive to use in conventional graph-based parsers. Experiments on the English Penn Treebank show that parsers based on our model perform better than conventional graph-based parsers.
REF proposed a model to automatically learn high-order feature combinations via a novel activation function, allowing their model to use a set of atomic features instead of millions of hand-crafted features.
An Effective Neural Network Model for Graph-based Dependency Parsing
ACL
Computer Science