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Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages

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arxiv 2212.10168 v2 pith:PYPTPORF submitted 2022-12-20 cs.CL

Arnav Mhaske , Harshit Kedia , Sumanth Doddapaneni , Mitesh M. Khapra , Pratyush Kumar , Rudra Murthy V , Anoop Kunchukuttan This is my paper · ORCID
classification cs.CL
keywords datasetlanguagesnaamapadamannotatedentityautomaticallyavailableentities
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abstract

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. The dataset contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location, and, Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language translation. We also create manually annotated testsets for 9 languages. We demonstrate the utility of the obtained dataset on the Naamapadam-test dataset. We also release IndicNER, a multilingual IndicBERT model fine-tuned on Naamapadam training set. IndicNER achieves an F1 score of more than $80$ for $7$ out of $9$ test languages. The dataset and models are available under open-source licences at https://ai4bharat.iitm.ac.in/naamapadam.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Tailored untruths: How personalisation challenges LLM safeguards

    cs.CL 2025-10 conditional novelty 7.0 of 10

    A 1.6-million-text study of eight LLMs in four languages finds that adding demographic personae to disinformation prompts raises jailbreak rates from 78% to 82%.

  2. HomoEnsNER: Does Language Alignment Outperform Architectural Complexity in Gujarati Named Entity Recognition?

    cs.CL 2026-08 conditional novelty 5.0 of 10

    A majority-vote ensemble of five independently fine-tuned GujaratiBERT models achieved the best entity-level F1 (0.8442) on Gujarati Naamapadam NER, ahead of the single-model baseline (0.8347) and all heterogeneous al...

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