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

arXiv: https://arxiv.org/abs/2212.10168 · HTML: https://mlsanity.com/paper/2212.10168/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 51 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [Data Scaling Laws in NMT: The Effect of Noise and Architecture](https://mlsanity.com/paper/2202.01994/) (2022)
- [Neural Cross-Lingual Named Entity Recognition with Minimal Resources](https://mlsanity.com/paper/1808.09861/) (2018)
- [IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://mlsanity.com/paper/2203.05437/) (2022)
- [Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language](https://mlsanity.com/paper/2004.12440/) (2020)
- [Entity Projection via Machine Translation for Cross-Lingual NER](https://mlsanity.com/paper/1909.05356/) (2019)
- [NNE: A Dataset for Nested Named Entity Recognition in English Newswire](https://mlsanity.com/paper/1906.01359/) (2019)
- [Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages](https://mlsanity.com/paper/2109.10534/) (2021)
- [L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models](https://mlsanity.com/paper/2204.06029/) (2022)
- [HiNER: A Large Hindi Named Entity Recognition Dataset](https://mlsanity.com/paper/2204.13743/) (2022)
- [AsNER -- Annotated Dataset and Baseline for Assamese Named Entity recognition](https://mlsanity.com/paper/2207.03422/) (2022)

## Built on by (most cited first, 28 of 28)

- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages](https://mlsanity.com/paper/2404.16816/) (2024)
- [Romanization-based Large-scale Adaptation of Multilingual Language Models](https://mlsanity.com/paper/2304.08865/) (2023)
- [A Material Lens on Coloniality in NLP](https://mlsanity.com/paper/2311.08391/) (2023)
- [BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2309.06085/) (2023)
- [Tokenization Matters: Improving Zero-Shot NER for Indic Languages](https://mlsanity.com/paper/2504.16977/) (2025)
- [Multilingual Pretraining for Pixel Language Models](https://mlsanity.com/paper/2505.21265/) (2025)
- [A Comprehensive Analysis of Adapter Efficiency](https://mlsanity.com/paper/2305.07491/) (2023)
- [A Three-Pronged Approach to Cross-Lingual Adaptation with Multilingual LLMs](https://mlsanity.com/paper/2406.17377/) (2024)
- [Overcoming Vocabulary Constraints with Pixel-level Fallback](https://mlsanity.com/paper/2504.02122/) (2025)
- [Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation](https://mlsanity.com/paper/2603.13045/) (2026)
- [Pragyaan: Designing and Curating High-Quality Cultural Post-Training Datasets for Indian Languages](https://mlsanity.com/paper/2510.07000/) (2025)
- [Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages](https://mlsanity.com/paper/2405.04829/) (2024)
- [IndicSentEval: How Effectively do Multilingual Transformer Models encode Linguistic Properties for Indic Languages?](https://mlsanity.com/paper/2410.02611/) (2024)
- [BanglaByT5: Byte-Level Modelling for Bangla](https://mlsanity.com/paper/2505.17102/) (2025)
- [Long Range Named Entity Recognition for Marathi Documents](https://mlsanity.com/paper/2410.09192/) (2024)
- [TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi](https://mlsanity.com/paper/2502.04245/) (2025)
- [Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus](https://mlsanity.com/paper/2511.00486/) (2025)
- [BHRAM-IL: A Benchmark for Hallucination Recognition and Assessment in Multiple Indian Languages](https://mlsanity.com/paper/2512.01852/) (2025)
- [Polite on the Surface, Broken in Practice: A Curated Dataset for Fixing Generation and Register Failures in Low-Resource Bangla Text Generation](https://mlsanity.com/paper/2605.22487/) (2026)
- [Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?](https://mlsanity.com/paper/2505.16814/) (2025)
- [Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective](https://mlsanity.com/paper/2509.01147/) (2025)
- [Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages](https://mlsanity.com/paper/2510.07061/) (2025)
- [AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers](https://mlsanity.com/paper/2601.10161/) (2026)
- [INDIC DIALECT: A Multi Task Benchmark to Evaluate and Translate in Indian Language Dialects](https://mlsanity.com/paper/2601.10388/) (2026)
- [BhashaSetu: Cross-Lingual Knowledge Transfer from High-Resource to Extreme Low-Resource Languages](https://mlsanity.com/paper/2602.05599/) (2026)
- [HomoEnsNER: Does Language Alignment Outperform Architectural Complexity in Gujarati Named Entity Recognition?](https://mlsanity.com/paper/2608.03105/) (2026)
- [Named Entity Recognition using Sliding Window Approach](https://mlsanity.com/paper/2609.29682/) (2026)

## Abstract (arXiv)

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.