Hybrid Neural Network Text Classification Combining TCN and GRU
Yapei Liu, Jianhong Ma, Yongcai Tao, Lei Shi, Lin Wei, Linna Li · 2020
Aiming at the problems of insufficient feature extraction and low classification accuracy in the classification of Chinese news texts by the neural network, this paper proposes a hybrid neural network text classification model which integrates time series convolutional network TGNet. The new model utilizes Temporal Convolutional Network to capture the relationship between hidden features on different time scale, and uses Gated Tanh-ReLU Units (GTRU) as the activation layer to improve the expression ability of neural network to the model. Meanwhile, the Gated Recurrent Unit networks (GRU) is used to learn the semantic features of context. Finally, the extracted features are fused and input into Softmax for classification. Experimental results show that the text classification model proposed in this paper has achieved better classification results in the published Chinese news data sets SougoCS and FuDan.