LSTM with compensation method for text classification
Wei Huang, Mengyu Liu, Wenqian Shang, Haibin Zhu, Weiguo Lin, Chunjie Zhang · International Journal of Wireless and Mobile Computing · 2021
As a foundational task, text classification is widely used in the field of Natural Language Processing (NLP). In the recent research on text classification, neural network-based methods have produced promising results. Nevertheless, most previous works ignore the fact that information may be lost or misinterpreted after the calculation of the neural network. In the research of this paper, we avoid such problems by using historical information, such as the original information of a text and the output information of the hidden layers, then performing text classification. This proposed method is called Long Short-Term Memory with compensation, or simply, LSTM-Com, which dynamically selects the important historical information as compensation for the neural network. In the classification experiment, the improved algorithm showed excellent performance compared to the baseline.