A Hybrid Neural Network for Sentence Classification
R. Zhou, X. Du
The sentence classification is the foundation of many Natural Language Processing applications. Prior neural network which use one type network for sentence classification can't use the abundant information in a sentence. In this paper, we proposed a hybrid neural network in combination with recurrent neural network and convolutional neural networks for sentence classification. The recurrent neural network can model long distance global information in a text, but it can’t effectively extract the local information and convolutional neural network inversely. The proposed hybrid neural network takes full advantage of the advantages of these two networks while extracting global feature and local feature at the same time. In order to get the global feature, we also proposed three different methods to make use of hidden states generated by recurrent neural network. We conducted experiments on four public open datasets. The results show that our hybrid neural network does better than models by using the recurrent neural network or the convolutional neural networks alone, higher and completive classification accuracy is obtained.