# KazNERD: Kazakh Named Entity Recognition Dataset

arXiv: https://arxiv.org/abs/2111.13419 · HTML: https://mlsanity.com/paper/2111.13419/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline](https://mlsanity.com/paper/2009.10334/) (2020)
- [KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset](https://mlsanity.com/paper/2104.08459/) (2021)
- [Introducing RONEC -- the Romanian Named Entity Corpus](https://mlsanity.com/paper/1909.01247/) (2019)
- [Attending to Entities for Better Text Understanding](https://mlsanity.com/paper/1911.04361/) (2019)
- [A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products](https://mlsanity.com/paper/2004.03287/) (2020)
- [A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English](https://mlsanity.com/paper/2108.01280/) (2021)
- [Neural Named Entity Recognition for Kazakh](https://mlsanity.com/paper/2007.13626/) (2020)

## Built on by (most cited first, 15 of 15)

- [KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics](https://mlsanity.com/paper/2201.05771/) (2022)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [KazQAD: Kazakh Open-Domain Question Answering Dataset](https://mlsanity.com/paper/2404.04487/) (2024)
- [Recent Advancements and Challenges of Turkic Central Asian Language Processing](https://mlsanity.com/paper/2407.05006/) (2024)
- [KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional Knowledge of Kazakhstan](https://mlsanity.com/paper/2502.12829/) (2025)
- [KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes](https://mlsanity.com/paper/2403.19335/) (2024)
- [KazParC: Kazakh Parallel Corpus for Machine Translation](https://mlsanity.com/paper/2403.19399/) (2024)
- [Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh](https://mlsanity.com/paper/2502.13647/) (2025)
- [Sherkala-Chat: Building a State-of-the-Art LLM for Kazakh in a Moderately Resourced Setting](https://mlsanity.com/paper/2503.01493/) (2025)
- [Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration](https://mlsanity.com/paper/2305.15749/) (2023)
- [OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages](https://mlsanity.com/paper/2412.09587/) (2024)
- [SozKZ: Training Efficient Small Language Models for Kazakh from Scratch](https://mlsanity.com/paper/2603.20854/) (2026)
- [Human-Annotated NER Dataset for the Kyrgyz Language](https://mlsanity.com/paper/2509.19109/) (2025)
- [TurkicNLP: An NLP Toolkit for Turkic Languages](https://mlsanity.com/paper/2602.19174/) (2026)
- [CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2403.15322/) (2024)

## Abstract (arXiv)

We present the development of a dataset for Kazakh named entity recognition. The dataset was built as there is a clear need for publicly available annotated corpora in Kazakh, as well as annotation guidelines containing straightforward--but rigorous--rules and examples. The dataset annotation, based on the IOB2 scheme, was carried out on television news text by two native Kazakh speakers under the supervision of the first author. The resulting dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. State-of-the-art machine learning models to automatise Kazakh named entity recognition were also built, with the best-performing model achieving an exact match F1-score of 97.22% on the test set. The annotated dataset, guidelines, and codes used to train the models are freely available for download under the CC BY 4.0 licence from https://github.com/IS2AI/KazNERD.