Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network
Zhisong Zhang, Hai Zhao, Lianhui Qin · 2016
This paper presents neural probabilistic parsing models which explore up to thirdorder 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 Treebanks and obtain competitive accuracies.