# Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset

arXiv: https://arxiv.org/abs/2308.13545 · HTML: https://mlsanity.com/paper/2308.13545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 59 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 20 of 20)

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- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
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- [Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation](https://mlsanity.com/paper/2005.10283/) (2020)
- [A C-LSTM Neural Network for Text Classification](https://mlsanity.com/paper/1511.08630/) (2015)
- [A Hybrid Convolutional Variational Autoencoder for Text Generation](https://mlsanity.com/paper/1702.02390/) (2017)
- [Adversarial Generation of Natural Language](https://mlsanity.com/paper/1705.10929/) (2017)
- [Deep Learning applied to NLP](https://mlsanity.com/paper/1703.03091/) (2017)
- [Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models](https://mlsanity.com/paper/1610.00369/) (2016)
- [Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation](https://mlsanity.com/paper/1904.07293/) (2019)
- [Supervised learning Methods for Bangla Web Document Categorization](https://mlsanity.com/paper/1410.2045/) (2014)
- [Dependency-based Convolutional Neural Networks for Sentence Embedding](https://mlsanity.com/paper/1507.01839/) (2015)

## Abstract (arXiv)

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.