基于循环神经网络变体和卷积神经网络的文本分类方法

Text classification method based on recurrent neural network variants and convolutional neural network

针对长文本在文本分类时提取语义关键特征难度大,分类效果差等问题,建立基于循环神经网络变体和卷积神经网络(BGRU-CNN)的混合模型,实现中文长文本的准确分类。首先,通过PV-DM模型将文本表示为句向量,并将其作为神经网络的输入;然后,建立BGRU-CNN模型,经双向门控循环单元(B-GRU)实现文本的序列信息表示,利用卷积神经网络(CNN)提取文本的关键特征,通过Softmax分类器实现文本的准确分类;最后,经SogouC和THUCNews中文语料集测试,文本分类准确率分别达到89.87%和94.65%。测试结果表明,循环层提取的文本序列特征通过卷积层得到了进一步优化,文本的分类性能得到了提高。

In view of the long text semantic key features is difficult to extract,poor classification results in a text classification,a mixed model based on recurrent neural network variants and convolutional neural networks (BGRU-CNN) was established to achieve accurate classification of Chinese long texts. First,the text is represented as a sentence vector by PV-DM model as input to the neural network. Then,the BGRU-CNN model is established,the sequence information of the text is represented by the bidirectional gate recurrent unit (B-GRU). The key features of the text are extracted by the convolution neural network (CNN),and the text is classified by the Softmax classifier. Finally,by SogouC and THUCNews corpus test,the accuracy of text classification reaches 89.87% and 94.65% respectively. The test results show that the text sequence features extracted by the recurrent layer are further optimized through convolution layer,and the classification performance of the text is improved.