Text classification model based on CNN and BiGRU fusion attention mechanism - Ma Yuqun; Chen Hailong; Wang Qing; Zheng Xin (ITM Web of Conferences, 2022)
This model proposes a text classification model with deep learning algorithm, which combines the characteristics of Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) in cyclic neural network, extracts local and global features of text feature words respectively, and calculates the importance of words to text classification task after fusing attention mechanism (Attention). Make the model focus on the feature words with high weight. Through the fusion of models, the accuracy of text classification is improved. The experimental results on IMDB film review dataset, Fudan University Chinese dataset and THUCNews dataset show that the proposed model has different degrees of improvement compared with the previously proposed models based on CNN, or LSTM and related fusion models in terms of accuracy, recall rate and F1 value.