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.- Anthology ID:
- 2020.lrec-1.551
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 4478–4485
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.551
- DOI:
- Cite (ACL):
- Elena Leitner, Georg Rehm, and Julian Moreno-Schneider. 2020. A Dataset of German Legal Documents for Named Entity Recognition. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4478–4485, Marseille, France. European Language Resources Association.
- Cite (Informal):
- A Dataset of German Legal Documents for Named Entity Recognition (Leitner et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.lrec-1.551.pdf
- Code
- elenanereiss/Legal-Entity-Recognition
- Data
- Dataset of Legal Documents