region:us
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.
@inproceedings{leitner2019fine,
  author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
  title = {{Fine-grained Named Entity Recognition in Legal Documents}},
  booktitle = {Semantic Systems. The Power of AI and Knowledge
                  Graphs. Proceedings of the 15th International Conference
                  (SEMANTiCS 2019)},
  year = 2019,
  editor = {Maribel Acosta and Philippe Cudré-Mauroux and Maria
                  Maleshkova and Tassilo Pellegrini and Harald Sack and York
                  Sure-Vetter},
  keywords = {aip},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  number = {11702},
  address = {Karlsruhe, Germany},
  month = 9,
  note = {10/11 September 2019},
  pages = {272--287},
  pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}
}
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