Effective text classification using BERT, MTM LSTM, and DT

Abstract

Text classification plays a critical role in managing large volumes of electronically produced texts. As the number of such texts increases, manual analysis becomes impractical, necessitating an intelligent approach for processing information. Deep learning models have witnessed widespread application in text classification, including the use of recurrent neural networks like Many to One Long Short-Term Memory (MTO LSTM). Nonetheless, this model is limited by its reliance on only the last token for text labelling. To overcome this limitation, this study introduces a novel hybrid model that combines Bidirectional Encoder Representations from Transformers (BERT), Many To Many Long Short-Term Memory (MTM LSTM), and Decision Templates (DT) for text classification. In this new model, the text is first embedded using the BERT model and then trained using MTM LSTM to approximate the target at each token. Finally, the approximations are fused using DT. The proposed model is evaluated using the well-known IMDB movie review dataset for binary classification and Drug Review Dataset for multiclass classification. The results demonstrate superior performance in terms of accuracy, recall, precision, and F1 score compared to previous models. The hybrid model presented in this study holds significant potential for a wide range of text classification tasks and stands as a valuable contribution to the field.