News Text Classification Method Based on the GRU_CNN Model
Lujuan Deng, Qingxia Ge, Jiaxue Zhang, Zuhe Li, Zeqi Yu, Tiantian Yin, Hanxue Zhu
Abstract
The convolutional neural network can extract local features of text but cannot capture structure information or semantic relationships between words, and a single CNN model’s classification performance is low, whereas GRU can effectively extract semantic information and global structure relationships of text. To address this problem, this paper proposes a news text classification method based on the GRU_CNN model, which combines the advantages of CNN and GRU. The model first trains word vectors as the embedding layer with the Word2vec model and then extracts semantic information from text sentences with the GRU model. Following that, this model employs the CNN model to extract crucial semantic information features and finally completes the classification through the Softmax layer. The experimental results reveal that the GRU_CNN hybrid model outperforms single CNN, LSTM, and GRU models in terms of classification effect and accuracy.