# Human-Annotated NER Dataset for the Kyrgyz Language

arXiv: https://arxiv.org/abs/2509.19109 · HTML: https://mlsanity.com/paper/2509.19109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023)
- [Semi-supervised sequence tagging with bidirectional language models](https://mlsanity.com/paper/1705.00108/) (2017)
- [KazNERD: Kazakh Named Entity Recognition Dataset](https://mlsanity.com/paper/2111.13419/) (2021)
- [GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets](https://mlsanity.com/paper/2311.09860/) (2023)
- [What do we Really Know about State of the Art NER?](https://mlsanity.com/paper/2205.00034/) (2022)
- [NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities](https://mlsanity.com/paper/2210.11913/) (2022)
- [KyrgyzNLP: Challenges, Progress, and Future](https://mlsanity.com/paper/2411.05503/) (2024)
- [Benchmarking Multilabel Topic Classification in the Kyrgyz Language](https://mlsanity.com/paper/2308.15952/) (2023)

## Built on by (most cited first, 1 of 1)

- [TurkicNLP: An NLP Toolkit for Turkic Languages](https://mlsanity.com/paper/2602.19174/) (2026)

## Abstract (arXiv)

We introduce KyrgyzNER, the first manually annotated named entity recognition dataset for the Kyrgyz language. Comprising 1,499 news articles from the 24.KG news portal, the dataset contains 10,900 sentences and 39,075 entity mentions across 27 named entity classes. We show our annotation scheme, discuss the challenges encountered in the annotation process, and present the descriptive statistics. We also evaluate several named entity recognition models, including traditional sequence labeling approaches based on conditional random fields and state-of-the-art multilingual transformer-based models fine-tuned on our dataset. While all models show difficulties with rare entity categories, models such as the multilingual RoBERTa variant pretrained on a large corpus across many languages achieve a promising balance between precision and recall. These findings emphasize both the challenges and opportunities of using multilingual pretrained models for processing languages with limited resources. Although the multilingual RoBERTa model performed best, other multilingual models yielded comparable results. This suggests that future work exploring more granular annotation schemes may offer deeper insights for Kyrgyz language processing pipelines evaluation.