---
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-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [https://github.com/elenanereiss/Legal-Entity-Recognition](https://github.com/elenanereiss/Legal-Entity-Recognition)
- **Paper:** [https://arxiv.org/pdf/2003.13016v1.pdf](https://arxiv.org/pdf/2003.13016v1.pdf)
- **Point of Contact:** [elena.leitner@dfki.de](elena.leitner@dfki.de)

### 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](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 |


<!-- 

## Dataset Creation

### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

 --> 
### Source Data

Court decisions from 2017 and 2018 were selected for the dataset, published online by the [Federal Ministry of Justice and Consumer Protection](http://www.rechtsprechung-im-internet.de). 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). 

<!-- #### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 --> 

### Annotations

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

<!-- #### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 --> 

### Licensing Information

[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)

### 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}
}

```

### Contributions