Chinese text classification based on attention mechanism and feature-enhanced fusion neural network

Abstract

Owing to the uneven distribution of key features in Chinese texts, key features play different roles in text recognition in Chinese text classification tasks. We propose a feature-enhanced fusion model based on attention mechanism for Chinese text classification, a long short-term memory (LSTM) network, a convolutional neural network (CNN), and a feature-difference enhancement attention algorithm model. The Chinese text is digitized into a vector form containing certain semantic context information into the embedding layer to train and test the neural network by preprocessing. The feature-enhanced fusion model is implemented by double-layer LSTM and CNN modules to enhance the fusion of text features extracted from the attention mechanism for classifying the classifiers. The feature-difference enhancement attention algorithm model not only adds more weight to important text features but also strengthens the differences between them and other text features. This operation can further improves the effect of important features on Chinese text recognition. The two models are classified by the softmax function. The text classification experiments are conducted based on the Chinese text corpus. The experimental results show that compared with the contrast model, the proposed algorithm can significantly improve the recognition ability of Chinese text features.