A Study on a Joint Deep Learning Model for Myanmar Text Classification

Abstract

Text classification is one of the most critical areas of research in the field of natural language processing (NLP). Recently, most of the NLP tasks achieve remarkable performance by using deep learning models. Generally, deep learning models require a huge amount of data to be utilized. This paper uses pre-trained word vectors to handle the resource-demanding problem and studies the effectiveness of a joint Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) for Myanmar text classification. The comparative analysis is performed on the baseline Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and their combined model CNN-RNN.