Implementation of Text Classification Model Based on Recurrent Neural Networks

Abstract

In this report, a deep learning network architecture, which combined with the architecture of convolutional neural network, the architecture of recurrent neural network is proposed and implemented to do the text classification with the pre-trained word vectors. The proposed method set the word vector be as a static lookup table without updating, and the network still can ignore the noise which caused by missing words. The experimental results show that the accuracy of this study is consistent with the accuracy of other studies. It is shown the feasibility of this architecture. And has the following advantages: the accuracy rate of this architecture is higher than that of recurrent neural network only; compared with the convolutional neural network, the accuracy results are more stable; and less epoch is used to get stable results. But the shortcoming of this proposed architecture is that the training time will consumed much time.