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. 2026 Feb 25;21(2):e0342786.
doi: 10.1371/journal.pone.0342786. eCollection 2026.

ANCHOLIK-NER: A benchmark dataset for Bangla regional named entity recognition

Bidyarthi Paul  1 , Faika Fairuj Preotee  1 , Shuvashis Sarker  1 , Shamim Rahim Refat  2 , Shifat Islam  3 , Tashreef Muhammad  1 , Mohammad Ashraful Hoque  1 , Shahriar Manzoor  1
Affiliations

ANCHOLIK-NER: A benchmark dataset for Bangla regional named entity recognition

Bidyarthi Paul et al. PLoS One. .

Abstract

Named Entity Recognition (NER) in regional dialects is a critical yet underexplored area in Natural Language Processing (NLP), especially for low-resource languages like Bangla. While NER systems for Standard Bangla have made progress, no existing resources or models specifically address the challenge of regional dialects such as Barishal, Chittagong, Mymensingh, Noakhali, and Sylhet, which exhibit unique linguistic features that existing models fail to handle effectively. To fill this gap, we introduce ANCHOLIK-NER, the first benchmark dataset for NER in Bangla regional dialects, comprising 17,405 sentences and 101,817 words annotated with 10 entity tags across 5 regions. The dataset was sourced from publicly available resources and supplemented with manual translations, ensuring alignment of named entities across dialects. We evaluate three transformer-based models-Bangla BERT, Bangla Bert Base, and BERT Base Multilingual Cased-on this dataset. Bangla BERT achieved the highest performance overall, with F1-scores of 82.27% (Mymensingh), 81.48% (Barishal), 78.75% (Sylhet), 78.50% (Noakhali), and 75.31% (Chittagong). These results highlight strong recognition capability in Mymensingh and Barishal, while dialectal variation in Chittagong remains challenging. As no prior NER resources exist for Bangla regional dialects, this work provides a foundational dataset and baseline benchmarks to facilitate future research. Future work will focus on dialect-aware model adaptation and expanding coverage to additional regions.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Regional NER examples along with Standard Bangla and English.
Fig 2
Fig 2. Development of ANCHOLIK-NER: A systematic pipeline for dataset creation.
Fig 3
Fig 3. Inter-annotator agreement (Cohen’s Kappa) across different regions.
Fig 4
Fig 4. Average tagging speed (time per 1000 tokens) by region in minutes.
Fig 5
Fig 5. Chittagong.
Fig 6
Fig 6. Sylhet.
Fig 7
Fig 7. Barishal.
Fig 8
Fig 8. Noakhali.
Fig 9
Fig 9. Mymensingh.
Fig 10
Fig 10. Frequency of named entities Chittagong dialects.
Fig 11
Fig 11. Frequency of named entities Barishal dialects.
Fig 12
Fig 12. Frequency of named entities Mymensingh dialects.
Fig 13
Fig 13. Frequency of named entities Sylhet dialects.
Fig 14
Fig 14. Frequency of named entities Noakhali dialects.
Fig 15
Fig 15. Methodology.
Fig 16
Fig 16. Confusion matrices for the best performing model across Barishal regional dialect.
Fig 17
Fig 17. Confusion matrices for the best performing model across Mymensingh regional dialect.
Fig 18
Fig 18. Confusion matrices for the best performing model across Chittagong regional dialect.
Fig 19
Fig 19. Confusion matrices for the best performing model across Noakhali regional dialect.
Fig 20
Fig 20. Confusion matrices for the best performing model across Sylhet regional dialect.
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