Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
German
Size:
1M<n<10M
ArXiv:
DOI:
License:
| 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-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:** [[email protected]]([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](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 | |