Publikation

A Dataset of German Legal Documents for Named Entity Recognition

Elena Leitner, Georg Rehm, Julian Moreno Schneider

In: Nicoletta Calzolari , Frédéric Béchet , Philippe Blache , Christopher Cieri , Khalid Choukri , Thierry Declerck , Hitoshi Isahara , Bente Maegaard , Joseph Mariani , Asuncion Moreno , Jan Odijk , Stelios Piperidis (Hrsg.). Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020). International Conference on Language Resources and Evaluation (LREC-2020) Marseille, France Seiten 4480-4487 ISBN 979-10-95546-34-4 European Language Resources Association (ELRA) 5/2020.

Abstrakt

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.

Projekte

Weitere Links

LREC-2020-Leitner-et-al-final.pdf (pdf, 157 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence