Chinese News Text Classification based on Attention-based CNN-BiLSTM

With the rapid development of text categorization technology, there are still some problems, such as low classification efficiency, low accuracy and incomplete extraction of text features, in the case of large amount of data and too many categorized attributes. In this paper, a hybrid model of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long-term and Short-term Memory Neural Network) combined with Attention (Attention Mechanism) is used to classify and process long text data. CNN extracts feature information from text, then uses BiLSTM to extract context semantics information, combines Attentiom to distribute weight of text information, and enters softmax classifier to classify. The experimental results show that the feature extraction of this model is more comprehensive, and the classification effect has been improved to a certain extent.