Datasets:
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:
- 1M<n<10M
source_datasets:
- original
tags:
- ner, named entity recognition, legal ner, legal texts, label classification
task_categories:
- token-classification
task_ids:
- named-entity-recognition
train-eval-index:
- config: conll2003
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
Dataset Card for "German LER"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
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
Citation Information
@misc{https://doi.org/10.48550/arxiv.2003.13016,
doi = {10.48550/ARXIV.2003.13016},
url = {https://arxiv.org/abs/2003.13016},
author = {Leitner, Elena and Rehm, Georg and Moreno-Schneider, Julián},
keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {A Dataset of German Legal Documents for Named Entity Recognition},
publisher = {arXiv},
year = {2020},
copyright = {arXiv.org perpetual, non-exclusive license}
}