# A Dataset of German Legal Documents for Named Entity Recognition

arXiv: https://arxiv.org/abs/2003.13016 · HTML: https://mlsanity.com/paper/2003.13016/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2020.

## Built on by (most cited first, 24 of 24)

- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [Named Entity Recognition in Indian court judgments](https://mlsanity.com/paper/2211.03442/) (2022)
- [LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text](https://mlsanity.com/paper/2402.04335/) (2024)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)
- [German BERT Model for Legal Named Entity Recognition](https://mlsanity.com/paper/2303.05388/) (2023)
- [SynLexLM: Scaling Legal LLMs with Synthetic Data and Curriculum Learning](https://mlsanity.com/paper/2504.18762/) (2025)
- [Automatic Anonymization of Swiss Federal Supreme Court Rulings](https://mlsanity.com/paper/2310.04632/) (2023)
- [Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents](https://mlsanity.com/paper/2201.05658/) (2022)
- [DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction](https://mlsanity.com/paper/2211.15424/) (2022)
- [Computational Law: Datasets, Benchmarks, and Ontologies](https://mlsanity.com/paper/2503.04305/) (2025)
- [ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detection](https://mlsanity.com/paper/2604.23585/) (2026)
- [Geolocating News about Extreme Climate Events: A Comparative Analysis of Off-the-Shelf Tools for Toponym Identification in German](https://mlsanity.com/paper/2605.03414/) (2026)
- [CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice with Fine-Grained Named Entities](https://mlsanity.com/paper/2305.18315/) (2023)
- [SinhaLegal: A Benchmark Corpus for Information Extraction and Analysis in Sinhala Legislative Texts](https://mlsanity.com/paper/2603.04854/) (2026)
- [Bundesrecht: An Open Library and Corpus for German Statutory Reference Processing](https://mlsanity.com/paper/2605.31338/) (2026)
- [Optimizing small BERTs trained for German NER](https://mlsanity.com/paper/2104.11559/) (2021)
- [Summarisation of German Judgments in conjunction with a Class-based Evaluation](https://mlsanity.com/paper/2505.05947/) (2025)
- [Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity](https://mlsanity.com/paper/2507.15864/) (2025)
- [A Two-Stage Architecture for NDA Analysis: LLM-based Segmentation and Transformer-based Clause Classification](https://mlsanity.com/paper/2603.09990/) (2026)
- [The GELATO Dataset for Legislative NER](https://mlsanity.com/paper/2603.14130/) (2026)
- [ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts](https://mlsanity.com/paper/2608.03898/) (2026)
- [Redakto - The Incognito Tab for LLMs](https://mlsanity.com/paper/2608.18260/) (2026)
- [IntLawNER: A Named Entity Recognition Dataset and Benchmark in International Law](https://mlsanity.com/paper/2609.22529/) (2026)

## Abstract (arXiv)

We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.