A Dataset of German Legal Documents for Named Entity Recognition

TL;DR

This paper introduces a comprehensive German legal document dataset with extensive manual and automatic annotations, designed to advance Named Entity Recognition in legal texts.

Contribution

It provides a large, richly annotated dataset specifically tailored for NER in German legal documents, filling a gap in resources for this domain.

Findings

Dataset contains 67,000 sentences and 2 million tokens.

Includes 54,000 manually annotated entities across 19 classes.

Annotated with over 35,000 time expressions using TimeML.

Abstract

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.