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A Dataset of German Legal Documents for Named Entity Recognition

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arxiv 2003.13016 v1 pith:B4CU4XCV submitted 2020-03-29 cs.CL cs.IR

Elena Leitner , Georg Rehm , Julián Moreno-Schneider This is my paper · ORCID
classification cs.CLcs.IR
keywords legalcourtdatasetdocumentsgermanannotateddevelopedentity
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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  1. Summarisation of German Judgments in conjunction with a Class-based Evaluation

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Fine-tuning a German legal language model with legal-entity tags modestly improves the content focus of automatically generated guiding principles for German court judgments, but the summaries still fall short of prac...

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