Probabilistic Graph-Based Dependency Parsing With Convolutional Neural Network

This paper presents neural probabilistic parsing models which explore up to third-order graph-based parsing with maximum likelihood training criteria. Two neural network extensions are exploited for performance improvement. Firstly, a convolutional layer that absorbs the influences of all words in a sentence is used so that sentence-level information can be effectively captured. Secondly, a linear layer is added to integrate different order neural models and trained with perceptron method. The proposed parsers are evaluated on English and Chinese Penn Tree banks and obtain competitive accuracies.