Abstract: Text classification is a basic task in natural language processing.Nowadays,it is more and more popular to use deep learning technology to deal with text classification tasks.When processing text sequences,convolutional neural networks can extract local features,and recurrent neural networks can extract global features,all of which show good effect.However,convolutional neural networks can not capture the context-related semantic information of text very well,and recurrent networks are not sensitive to the key semantic information.In addition,although deeper networks can better extract features,they are prone to gradient disappearance or gradient explosion.To solve these problems,this paper proposed a hybrid model based on densely connected gated recurrent unit convolutional networks (DC-BiGRU_CNN).Firstly,a standard convolutional neural network is used to train the character-level word vector,and then the character-level word vector is spliced with the word-level word vector to form the network input layer.Inspired by the densely connected convolutional network,a proposed densely connected bidirectional gated recurrent unit is used in the stage of high-level semantic modeling of text,which can alleviate the defect of gradient disappearance or gradient explosion and enhance the transfer between features of each layer,thus achieving feature reuse.Next,the convolution and pooling operation are conducted for the deep high-level semantic representation to obtain the final semantic feature representation,which is then input to the softmax layer to complete text classification task.The experimental results on several public datasets show that DC-BiGRU_CNN has a significant performance improvement in terms of the accuracy for text classification tasks.In addition,this paper analyzed the effect of different components of the model onperfor-mance improvement,and studied the effect of parameters such as the maximum length of sentence,the number of layers ofthe network and the size of the convolution kernel on the model.

Key words: Bi-directional gated recurrent unit, Character-level word vector, Convolutional neural network, Dense connection, Text classification