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.
