TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi

Mohammed Amaan Dhamaskar, Rasika Ransing
2/6/2025
cs.CLcs.AIcs.LG

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Abstract

India's rich cultural and linguistic diversity poses various challenges in the domain of Natural Language Processing (NLP), particularly in Named Entity Recognition (NER). NER is a NLP task that aims to identify and classify tokens into different entity groups like Person, Location, Organization, Number, etc. This makes NER very useful for downstream tasks like context-aware anonymization. This paper details our work to build a multilingual NER model for the three most spoken languages in India - Hindi, Bengali & Marathi. We train a custom transformer model and fine tune a few pretrained models, achieving an F1 Score of 92.11 for a total of 6 entity groups. Through this paper, we aim to introduce a single model to perform NER and significantly reduce the inconsistencies in entity groups and tag names, across the three languages.

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Cite this paper

@article{dhamaskar2025triner,
  title  = {TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi},
  author = {Mohammed Amaan Dhamaskar and Rasika Ransing},
  year   = {2025},
  eprint = {2502.04245},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.04245v1}
}

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