Feature Extraction Using Deep Generative Models for Bangla Text
  Classification on a New Comprehensive Dataset
Md. Rafi-Ur-Rashid Sami Azam Mirjam Jonkman
The selection of features for text classification is a fundamental task in
text mining and information retrieval. Despite being the sixth most widely
spoken language in the world, Bangla has received little attention due to the
scarcity of text datasets. In this research, we collected, annotated, and
prepared a comprehensive dataset of 212,184 Bangla documents in seven different
categories and made it publicly accessible. We implemented three deep learning
generative models: LSTM variational autoencoder (LSTM VAE), auxiliary
classifier generative adversarial network (AC-GAN), and adversarial autoencoder
(AAE) to extract text features, although their applications are initially found
in the field of computer vision. We utilized our dataset to train these three
models and used the feature space obtained in the document classification task.
We evaluated the performance of the classifiers and found that the adversarial
autoencoder model produced the best feature space.