TurkicNLP: An NLP Toolkit for Turkic Languages
cs.CL
Submitted: 2026-02-22
Updated: 2026-10-12
Comments: The toolkit is available here: https://github.com/turkic-nlp/turkicnlp
Code: https://github.com/turkic-nlp/turkicnlp
Project page: https://turkishnlp.github.io
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Turkic languages are spoken by about 200 million people and are written in Latin, Cyrillic, and Perso-Arabic scripts, yet the NLP tools that exist for them are scattered across languages, scripts,
Terminology
Abstract
Turkic languages are spoken by about 200 million people and are written in Latin, Cyrillic, and Perso-Arabic scripts, yet the NLP tools that exist for them are scattered across languages, scripts, and incompatible software. We present TurkicNLP, an open-source Python library that offers a single pipeline for 24 Turkic languages, covering tokenization, morphological analysis, POS tagging, dependency parsing, named entity recognition, language identification, sentence embeddings, and machine translation. The library is designed for cross-lingual use: it detects and converts between scripts, maps 23 languages into the shared Common Turkic Alphabet, and includes multilingual models that share parameters across the family and transfer to languages without annotated data. Our evaluation shows the benefits of this design. The multilingual parser outperforms per-language parsers, the multilingual morphological analyzer complements finite-state analyzers where their lexicons are small, and both produce usable analyses for languages unseen in training. Transliteration is almost fully reversible, and converting text to the Common Turkic Alphabet removes most of the additional tokenization cost that large language models incur on Cyrillic- and Arabic-script Turkic languages. Code, models, and documentation are available at: https://github.com/turkic-nlp/turkicnlp.
Sources
- Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation
- Sherkala-Chat: Building a State-of-the-Art LLM for Kazakh in a Moderately Resourced Setting
- UzBERT: pretraining a BERT model for Uzbek
- USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments
- No Language Left Behind: Scaling Human-Centered Machine Translation
- Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers
- Human-Annotated NER Dataset for the Kyrgyz Language
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