Deep feature extraction with tri-channel textual feature map for text classification
Kunyan Li, Chen Kang · Pattern Recognition Letters · 2023
The complexity and diversity of texts make it difficult for shallow text classification models to capture deeper text features. Therefore, this paper takes advantage of the BiLSTM-CNN hybrid network based on the self-attention mechanism to extract text features, and constructs a new text feature representation similar to RGB-channel images, that is, tri-channel text feature maps. Drawing on the effectiveness of ResNet and SA-Net in deep networks, we designed a deep feature extraction network to capture deeper features in the text. We use the tri-channel text feature map as the input to the deep feature extraction network , and propose the Deeper Feature Text Classification (DFTC) end-to-end text classification model. Experiments prove that the DFTC model is competitive compared to the most advanced methods on five challenging text classification datasets.