README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - de
license:
  - cc-by-4.0
multilinguality:
  - monolingual
paperswithcode_id: dataset-of-legal-documents
pretty_name: German Named Entity Recognition in Legal Documents
size_categories:
  - 2M
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition

Dataset Card for "German LER"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://github.com/elenanereiss/Legal-Entity-Recognition
• Paper: https://arxiv.org/pdf/2003.13016v1.pdf
• Point of Contact: [email protected]

Dataset Summary

A dataset of Legal Documents from German federal court decisions for Named Entity Recognition. The dataset is human-annotated with 19 fine-grained entity classes. The dataset consists of approx. 67,000 sentences and contains 54,000 annotated entities. NER tags use the BIO tagging scheme.

For more details see https://arxiv.org/pdf/2003.13016v1.pdf.

Supported Tasks and Leaderboards

• Tasks: Named Entity Recognition
• Leaderboards:

Languages

German

Dataset Structure

Data Instances

{
 'id': '1',
 'tokens': ['Eine', 'solchermaßen', 'verzögerte', 'oder', 'bewusst', 'eingesetzte', 'Verkettung', 'sachgrundloser', 'Befristungen', 'schließt', '§', '14', 'Abs.', '2', 'Satz', '2', 'TzBfG', 'aus', '.'],
 'ner_tags': [38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3, 22, 22, 22, 22, 22, 22, 38, 38]
}

Data Fields

{
 'id': Value(dtype='string', id=None),
 'tokens': Sequence(feature=Value(dtype='string', id=None), 
                    length=-1, id=None),
 'ner_tags': Sequence(feature=ClassLabel(num_classes=39, 
                                         names=['B-AN', 
                                                'B-EUN', 
                                                'B-GRT', 
                                                'B-GS', 
                                                'B-INN', 
                                                'B-LD', 
                                                'B-LDS', 
                                                'B-LIT', 
                                                'B-MRK', 
                                                'B-ORG', 
                                                'B-PER', 
                                                'B-RR', 
                                                'B-RS', 
                                                'B-ST', 
                                                'B-STR', 
                                                'B-UN', 
                                                'B-VO', 
                                                'B-VS', 
                                                'B-VT', 
                                                'I-AN', 
                                                'I-EUN', 
                                                'I-GRT', 
                                                'I-GS', 
                                                'I-INN', 
                                                'I-LD', 
                                                'I-LDS', 
                                                'I-LIT', 
                                                'I-MRK', 
                                                'I-ORG', 
                                                'I-PER', 
                                                'I-RR', 
                                                'I-RS', 
                                                'I-ST', 
                                                'I-STR', 
                                                'I-UN', 
                                                'I-VO', 
                                                'I-VS', 
                                                'I-VT', 
                                                'O'], 
                                         id=None), 
                      length=-1, 
                      id=None)
}

Data Splits

	train	validation	test
Input Sentences	53384	6666	6673

Source Data

Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).

Annotations

For more details see https://github.com/elenanereiss/Legal-Entity-Recognition/blob/master/docs/Annotationsrichtlinien.pdf.

Licensing Information

CC BY-SA 4.0 license

Citation Information

@article{DBLP:journals/corr/abs-2003-13016,
  author    = {Elena Leitner and
               Georg Rehm and
               Juli{\'{a}}n Moreno Schneider},
  title     = {A Dataset of German Legal Documents for Named Entity Recognition},
  journal   = {CoRR},
  volume    = {abs/2003.13016},
  year      = {2020},
  url       = {https://arxiv.org/abs/2003.13016},
  eprinttype = {arXiv},
  eprint    = {2003.13016},
  timestamp = {Sat, 23 Jan 2021 01:20:26 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2003-13016.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions