Persian Text Classification Based on Deep Neural Networks

Nowadays, according to the growing volume of electronic documents, the classification of text has attracted the attention of information retrieval researchers. Considering the importance of text classification and the efforts done in this field in several languages in the world, the necessity of Persian text classification is understood. In general, we can classify text classification methods into two classes, including traditional methods (based on feature selection and machine learning) and methods based on deep learning. Deep learning methods, due to the ability of weight sharing, significantly reduce the number of trainable parameters and thus increase generalization and provide better results than other methods. There are a few methods based on deep learning for Persian text classification. In this study, we propose to use CNN and BLSTM with an attention layer for Persian text classification named ParsCNN and ParsBiLSTM. The experimental results on the Hamshahri dataset show that the ParsCNN method has a precision of 0.69, a recall of 0.7, and, an F-score of 0.69; Also, the ParsBiLSTM method has a precision of 0.72, a recall of 0.73 and, an F-score of 0.72, which indicates the methods based on deep Neural Networks have better performance than other approaches.