Bangla Document Classification Using Deep Recurrent Neural Network with BiLSTM

Classification of large unlabelled document collection is a difficult task. Natural language processing can be used as a powerful tool for the classification of an unlabelled large document collection. Document classification is the task of assigning some predefined class to unlabelled large bodies of documents. In this paper, a new way of classifying Bangla news documents was proposed using a Deep Recurrent Neural Network. At first the collected news data was preprocessed. Designing the model architecture training data was fitted out into the model. Finally, model performance was evaluated by calculating the accuracy and F1-score on testing dataset. The Deep Recurrent Neural Network with BiLSTM achieved 98.33% accuracy which is higher than other well-known classification algorithms in Bangla text classification.