Combining Discrete and Continuous Features for Deterministic Transition-based Dependency Parsing

Meishan Zhang, Yue Zhang

Abstract: We investigate a combination of a traditional linear sparse feature model and a multi-layer neural network model for deterministic transition-based dependency parsing, by integrating the sparse features into the neural model. Correlations are drawn between the hybrid model and previous work on integrating word embedding features into a discrete linear model. By analyzing the results of various parsers on web-domain parsing, we show that the integrated model is a better way to combine traditional and embedding features compared with previous methods.