TLNER: Turkish Legal Named Entity Recognition Dataset ⚖️ 🇹🇷

TLNER is a densely annotated, domain-specific dataset specifically designed for Named Entity Recognition (NER) tasks in Turkish judicial texts. It contains formal court decisions derived from the Council of State (Danıştay) and the Court of Cassation (Yargıtay), the highest judicial bodies in Turkey. For more details about the dataset, methodology, and experiments, you can refer to the corresponding research paper.

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Citation

If you use this dataset, please cite the following paper:

@article{incidelen2026workflow,
  title={A workflow-oriented and risk-aware system for Turkish legal named entity recognition: integrating transformer-based models with legal knowledge graphs},
  author={{\.I}ncidelen, Mert and Aydo{\u{g}}an, Murat},
  journal={Journal of King Saud University Computer and Information Sciences},
  year={2026},
  publisher={Springer}
  doi={10.1007/s44443-026-00915-z}
}

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Dataset Overview

• Number of Sentences: 1,887
• Number of Total Tokens: 100,867
• Number of Entity Tokens: 32,920
• Entity Density: 32.64%
• Languages: Turkish

Dataset Structure

The dataset is divided into three subsets for training, validation, and testing:

Split	Number of Sentences	Number of Tokens
Training	1,509	80,976
Validation	189	10,060
Testing	189	9,831

Entity Taxonomy

The dataset includes 7 domain-specific legal entity types annotated using the standard BIO scheme:

• CA (Case Number): Unique identifiers for judicial files, including both case and decision numbers.
• COU (Court): Judicial bodies, chambers, and specific courts.
• DATE (Date): Temporal expressions.
• DEC (Judicial Decision): Formal judicial outcomes and verdict expressions.
• LEG (Legislation): Statutory references like laws and articles.
• PER (Person): Names of individuals mentioned in the text.
• ROLE (Legal Role): Institutional roles and titles of the parties in the legal context.

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How to Use

This dataset can be used with libraries such as 🤗 Datasets or pandas. Below are examples of the use of the dataset:

from datasets import load_dataset

dataset = load_dataset("incidelen/TLNER")

train_data = dataset["train"]
val_data = dataset["validation"]
test_data = dataset["test"]