DCCL: Dual-channel hybrid neural network combined with self-attention for text classification

Text classification is a fundamental task in natural language processing. The Chinese text classification task suffers from sparse text features, ambiguity in word segmentation, and poor performance of classification models. A text classification model is proposed based on the self-attention mechanism combined with CNN and LSTM. The proposed model uses word vectors as input to a dual-channel neural network structure, using multiple CNNs to extract the N-Gram information of different word windows and enrich the local feature representation through the concatenation operation, the BiLSTM is used to extract the semantic association information of the context to obtain the high-level feature representation at the sentence level. The output of BiLSTM is feature weighted with self-attention to reduce the influence of noisy features. The outputs of the dual channels are concatenated and fed into the softmax layer for classification. The results of the multiple comparison experiments showed that the DCCL model obtained 90.07% and 96.26% F1-score on the Sougou and THUNews datasets, respectively. Compared to the baseline model, the improvement was 3.24% and 2.19%, respectively. The proposed DCCL model can alleviate the problem of CNN losing word order information and the gradient of BiLSTM when processing text sequences, effectively integrate local and global text features, and highlight key information. The classification performance of the DCCL model is excellent and suitable for text classification tasks.