# Deep Learning for Hindi Text Classification: A Comparison

arXiv: https://arxiv.org/abs/2001.10340 · HTML: https://mlsanity.com/paper/2001.10340/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2020.

## Built on by (most cited first, 22 of 22)

- [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022)
- [L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages](https://mlsanity.com/paper/2211.11418/) (2022)
- [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021)
- [L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library](https://mlsanity.com/paper/2205.14728/) (2022)
- [L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models](https://mlsanity.com/paper/2203.13778/) (2022)
- [L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models](https://mlsanity.com/paper/2204.06029/) (2022)
- [Experimental Evaluation of Deep Learning models for Marathi Text Classification](https://mlsanity.com/paper/2101.04899/) (2021)
- [Hate and Offensive Speech Detection in Hindi and Marathi](https://mlsanity.com/paper/2110.12200/) (2021)
- [Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2204.08669/) (2022)
- [Comparative Study of Long Document Classification](https://mlsanity.com/paper/2111.00702/) (2021)
- [L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi](https://mlsanity.com/paper/2211.11187/) (2022)
- [Evaluation of Deep Learning Models for Hostility Detection in Hindi Text](https://mlsanity.com/paper/2101.04144/) (2021)
- [Hostility Detection in Hindi leveraging Pre-Trained Language Models](https://mlsanity.com/paper/2101.05494/) (2021)
- [ShufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models](https://mlsanity.com/paper/2102.00238/) (2021)
- [Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition](https://mlsanity.com/paper/2203.12907/) (2022)
- [Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches](https://mlsanity.com/paper/2110.09338/) (2021)
- [Taxonomic survey of Hindi Language NLP systems](https://mlsanity.com/paper/2102.00214/) (2021)
- [On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations](https://mlsanity.com/paper/2201.00318/) (2022)
- [Universal Cross-Lingual Text Classification](https://mlsanity.com/paper/2406.11028/) (2024)
- [An Attention Ensemble Approach for Efficient Text Classification of Indian Languages](https://mlsanity.com/paper/2102.10275/) (2021)
- [MaiBERT: A Pre-training Corpus and Language Model for Low-Resourced Maithili Language](https://mlsanity.com/paper/2509.15048/) (2025)
- [HinFlair: pre-trained contextual string embeddings for pos tagging and text classification in the Hindi language](https://mlsanity.com/paper/2101.06949/) (2021)

## Abstract (arXiv)

Natural Language Processing (NLP) and especially natural language text analysis have seen great advances in recent times. Usage of deep learning in text processing has revolutionized the techniques for text processing and achieved remarkable results. Different deep learning architectures like CNN, LSTM, and very recent Transformer have been used to achieve state of the art results variety on NLP tasks. In this work, we survey a host of deep learning architectures for text classification tasks. The work is specifically concerned with the classification of Hindi text. The research in the classification of morphologically rich and low resource Hindi language written in Devanagari script has been limited due to the absence of large labeled corpus. In this work, we used translated versions of English data-sets to evaluate models based on CNN, LSTM and Attention. Multilingual pre-trained sentence embeddings based on BERT and LASER are also compared to evaluate their effectiveness for the Hindi language. The paper also serves as a tutorial for popular text classification techniques.