# Gazetteer-Enhanced Bangla Named Entity Recognition with BanglaBERT Semantic Embeddings K-Means-Infused CRF Model

arXiv: https://arxiv.org/abs/2401.17206 · HTML: https://mlsanity.com/paper/2401.17206/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://mlsanity.com/paper/2101.00204/) (2021)
- [BNLP: Natural language processing toolkit for Bengali language](https://mlsanity.com/paper/2102.00405/) (2021)

## Built on by (most cited first, 2 of 2)

- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)
- [Bangla MedER: Multi-BERT Ensemble Approach for the Recognition of Bangla Medical Entity](https://mlsanity.com/paper/2512.17769/) (2025)

## Abstract (arXiv)

Named Entity Recognition (NER) is a sub-task of Natural Language Processing (NLP) that distinguishes entities from unorganized text into predefined categorization. In recent years, a lot of Bangla NLP subtasks have received quite a lot of attention; but Named Entity Recognition in Bangla still lags behind. In this research, we explored the existing state of research in Bangla Named Entity Recognition. We tried to figure out the limitations that current techniques and datasets face, and we would like to address these limitations in our research. Additionally, We developed a Gazetteer that has the ability to significantly boost the performance of NER. We also proposed a new NER solution by taking advantage of state-of-the-art NLP tools that outperform conventional techniques.