# Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

arXiv: https://arxiv.org/abs/2105.14875 · HTML: https://mlsanity.com/paper/2105.14875/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 238 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 28 of 28)

- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Selective Question Answering under Domain Shift](https://mlsanity.com/paper/2006.09462/) (2020)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [Unsupervised Question Decomposition for Question Answering](https://mlsanity.com/paper/2002.09758/) (2020)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations](https://mlsanity.com/paper/1708.02691/) (2017)
- [Intermediate Loss Regularization for CTC-based Speech Recognition](https://mlsanity.com/paper/2102.03216/) (2021)
- [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1910.00883/) (2019)
- [Visual Question Answering: Datasets, Algorithms, and Future Challenges](https://mlsanity.com/paper/1610.01465/) (2016)
- [Deep Learning Models for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2004.06465/) (2020)
- [Listening while Speaking: Speech Chain by Deep Learning](https://mlsanity.com/paper/1707.04879/) (2017)
- [A neural attention model for speech command recognition](https://mlsanity.com/paper/1808.08929/) (2018)
- [The PyTorch-Kaldi Speech Recognition Toolkit](https://mlsanity.com/paper/1811.07453/) (2018)
- [BanFakeNews: A Dataset for Detecting Fake News in Bangla](https://mlsanity.com/paper/2004.08789/) (2020)
- [Optimizing expected word error rate via sampling for speech recognition](https://mlsanity.com/paper/1706.02776/) (2017)
- [Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users](https://mlsanity.com/paper/2104.13083/) (2021)
- [Natural Language Processing for Information Extraction](https://mlsanity.com/paper/1807.02383/) (2018)
- [Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition](https://mlsanity.com/paper/1901.10055/) (2019)
- [Detection of Bangla Fake News using MNB and SVM Classifier](https://mlsanity.com/paper/2005.14627/) (2020)
- [Unsupervised Abstractive Summarization of Bengali Text Documents](https://mlsanity.com/paper/2102.04490/) (2021)
- [Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages](https://mlsanity.com/paper/1803.04000/) (2018)
- [A Deep Generative Model of Speech Complex Spectrograms](https://mlsanity.com/paper/1903.03269/) (2019)
- [Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities](https://mlsanity.com/paper/1704.02360/) (2017)
- [Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech Recognition](https://mlsanity.com/paper/1308.3785/) (2013)
- [Detection of opinion spam based on anomalous rating deviation](https://mlsanity.com/paper/1608.00684/) (2016)
- [Sentiment Expression via Emoticons on Social Media](https://mlsanity.com/paper/1511.02556/) (2015)

## Built on by (most cited first, 8 of 8)

- [How Ready Are Generative Pre-trained Large Language Models for Explaining Bengali Grammatical Errors?](https://mlsanity.com/paper/2406.00039/) (2024)
- [Transcribing Bengali Text with Regional Dialects to IPA using District Guided Tokens](https://mlsanity.com/paper/2403.17407/) (2024)
- [RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers](https://mlsanity.com/paper/2310.14261/) (2023)
- [Question-Answering System for Bangla: Fine-tuning BERT-Bangla for a Closed Domain](https://mlsanity.com/paper/2410.03923/) (2024)
- [Social Media Sentiments Analysis on the July Revolution in Bangladesh: A Hybrid Transformer Based Machine Learning Approach](https://mlsanity.com/paper/2507.11084/) (2025)
- [BOISHOMMO: Holistic Approach for Bangla Hate Speech](https://mlsanity.com/paper/2504.08408/) (2025)
- [EDU-NER-2025: Named Entity Recognition in Urdu Educational Texts using XLM-RoBERTa with X (formerly Twitter)](https://mlsanity.com/paper/2504.18142/) (2025)
- [Language Specificity vs. Domain Diversity: Benchmarking Transformers for Bangla Medical NER](https://mlsanity.com/paper/2609.29101/) (2026)

## Abstract (arXiv)

The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive review of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough analysis of 75 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50% of the papers were published after 2015. Furthermore, we discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP.