A Novel Optimized Convolutional Neural Network Based On Attention Pooling for Text Classification
Chengjie Yao, Mengmeng Cai · Journal of Physics Conference Series · 2021
Abstract Convolutional neural network (CNN) is widely used in Natural Language Processing (NLP) and has achieved relatively good results. Since the convolutional layer of CNN can be initialized by encoding important semantic features, CNN can extract important semantic features in the convolutional layer. The pooling layer of CNN uses the feature of max-pooling to filter the features, but it does not consider the key information in the sentence and the context semantic information in the text. Therefore, a novel neural network algorithm combining with the attention mechanism and the pooling layer is proposed in this paper to make the model pay attention to the keywords in the sentence and automatically maintain the most meaningful text message, thereby improving the performance of text classification. Our paper uses important information features to initialize the convolution filter so that the model can notice the important semantic features. The resulting feature is enhanced by combining the attention mechanism in the pooling layer, so that the model can maintain the important information features of the text. Experiments show that the proposed model achieves excellent results for multiple text classification tasks, including sentiment classification and topic classification.