# AsNER -- Annotated Dataset and Baseline for Assamese Named Entity recognition

arXiv: https://arxiv.org/abs/2207.03422 · HTML: https://mlsanity.com/paper/2207.03422/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages](https://mlsanity.com/paper/1710.02187/) (2017)

## Built on by (most cited first, 6 of 6)

- [Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages](https://mlsanity.com/paper/2212.10168/) (2022)
- [AxomiyaBERTa: A Phonologically-aware Transformer Model for Assamese](https://mlsanity.com/paper/2305.13641/) (2023)
- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)
- [AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach](https://mlsanity.com/paper/2212.07043/) (2022)
- [Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset Repository](https://mlsanity.com/paper/2410.11291/) (2024)
- [NagaNLP: Bootstrapping NLP for Low-Resource Nagamese Creole with Human-in-the-Loop Synthetic Data](https://mlsanity.com/paper/2512.12537/) (2025)

## Abstract (arXiv)

We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The proposed NER dataset is likely to be a significant resource for deep neural based Assamese language processing. We benchmark the dataset by training NER models and evaluating using state-of-the-art architectures for supervised named entity recognition (NER) such as Fasttext, BERT, XLM-R, FLAIR, MuRIL etc. We implement several baseline approaches with state-of-the-art sequence tagging Bi-LSTM-CRF architecture. The highest F1-score among all baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding method. The annotated dataset and the top performing model are made publicly available.