Neural network model for dependency parsing incorporating global vector feature - Hengjun WANG; Nianwen SI; Yulong SONG; Yidong SHAN (Tongxin xuebao)
LSTM and piecewise CNN were utilized to extract word vector features and global vector features,respectively.Then the two features were input to feed forward network for training.In model training,the probabilistic training method was adopted.Compared with the original dependency paring model,the proposed model focused more on global features,and used all potential dependency trees to update model parameters.Experiments on Chinese Penn Treebank 5 (CTB5) dataset show that,compared with the parsing model using LSTM or CNN only,the proposed model not only remains the relatively low model complexity,but also achieves higher accuracies.