Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Languages:
German
Size:
10K<n<100K
Tags:
License:
| annotations_creators: | |
| - expert-generated | |
| - found | |
| language_creators: | |
| - found | |
| language: | |
| - de | |
| license: | |
| - cc-by-4.0 | |
| multilinguality: | |
| - monolingual | |
| pretty_name: Annotated German Legal Decision Corpus | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - multi-class-classification | |
| # Dataset Card for Annotated German Legal Decision Corpus | |
| ## Table of Contents | |
| - [Table of Contents](#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:** | |
| - **Repository:** https://zenodo.org/record/3936490#.X1ed7ovgomK | |
| - **Paper:** Urchs., S., Mitrović., J., & Granitzer., M. (2021). Design and Implementation of German Legal Decision | |
| Corpora. Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, | |
| 515–521. https://doi.org/10.5220/0010187305150521 | |
| - **Leaderboard:** | |
| - **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch) | |
| ### Dataset Summary | |
| This dataset consists of 200 randomly chosen judgments. In these judgments a legal expert annotated the components | |
| conclusion, definition and subsumption of the German legal writing style Urteilsstil. | |
| *"Overall 25,075 sentences are annotated. 5% (1,202) of these sentences are marked as conclusion, 21% (5,328) as | |
| definition, 53% (13,322) are marked as subsumption and the remaining 21% (6,481) as other. The length of judgments in | |
| sentences ranges from 38 to 862 sentences. The median of judgments have 97 sentences, the length of most judgments is on | |
| the shorter side."* (Urchs. et al., 2021) | |
| *"Judgments from 22 of the 131 courts are selected for the corpus. Most judgments originate from the VG Augsburg (59 / | |
| 30%) followed by the VG Ansbach (39 / 20%) and LSG Munich (33 / 17%)."* (Urchs. et al., 2021) | |
| *"29% (58) of all selected judgments are issued in the year 2016, followed by 22% (44) from the year 2017 and 21% (41) | |
| issued in the year 2015. [...] The percentages of selected judgments and decisions issued in 2018 and 2019 are roughly | |
| the same. No judgments from 2020 are selected."* (Urchs. et al., 2021) | |
| ### Supported Tasks and Leaderboards | |
| The dataset can be used for multi-class text classification tasks, more specifically, for argument mining. | |
| ### Languages | |
| The language in the dataset is German as it is used in Bavarian courts in Germany. | |
| ## Dataset Structure | |
| ### Data Instances | |
| Each sentence is saved as a json object on a line in one of the three files `train.jsonl`, `validation.jsonl` | |
| or `test.jsonl`. The file `meta.jsonl` contains meta information for each court. The `file_number` is present in all | |
| files for identification. Each sentence of the court decision was categorized according to its function. | |
| ### Data Fields | |
| The file `meta.jsonl` contains for each row the following fields: | |
| - `meta_title`: Title provided by the website, it is used for saving the decision | |
| - `court`: Issuing court | |
| - `decision_style`: Style of the decision; the corpus contains either *Urteil* (='judgment') or *Endurteil* ( | |
| ='end-judgment') | |
| - `date`: Date when the decision was issued by the court | |
| - `file_number`: Identification number used for this decision by the court | |
| - `title`: Title provided by the court | |
| - `norm_chains`: Norms related to the decision | |
| - `decision_guidelines`: Short summary of the decision | |
| - `keywords`: Keywords associated with the decision | |
| - `lower_court`: Court that decided on the decision before | |
| - `additional_information`: Additional Information | |
| - `decision_reference`: References to the location of the decision in beck-online | |
| - `tenor`: Designation of the legal consequence ordered by the court (list of paragraphs) | |
| - `legal_facts`: Facts that form the base for the decision (list of paragraphs) | |
| The files `train.jsonl`, `validation.jsonl` and `test.jsonl` contain the following fields: | |
| - `file_number`: Identification number for linkage with the file `meta.jsonl` | |
| - `input_sentence`: The sentence to be classified | |
| - `label`: In depth explanation of the court decision. Each sentence is assigned to one of the major components of | |
| German *Urteilsstil* (Urchs. et al., 2021) (list of paragraphs, each paragraph containing list of sentences, each | |
| sentence annotated with one of the following four labels): | |
| - `conclusion`: Overall result | |
| - `definition`: Abstract legal facts and consequences | |
| - `subsumption`: Determination sentence / Concrete facts | |
| - `other`: Anything else | |
| - `context_before`: Context in the same paragraph before the input_sentence | |
| - `context_after`: Context in the same paragraph after the input_sentence | |
| ### Data Splits | |
| No split provided in the original release. | |
| Splits created by Joel Niklaus. We randomly split the dataset into 80% (160 decisions, 19271 sentences) train, 10% | |
| validation (20 decisions, 2726 sentences) and 10% test (20 decisions, 3078 sentences). We made sure, that a decision | |
| only occurs in one split and is not dispersed over multiple splits. | |
| Label Distribution | |
| | label | train | validation | test | | |
| |:---------------|-----------:|-------------:|----------:| | |
| | conclusion | 975 | 115 | 112 | | |
| | definition | 4105 | 614 | 609 | | |
| | subsumption | 10034 | 1486 | 1802 | | |
| | other | 4157 | 511 | 555 | | |
| | total | **19271** | **2726** | **3078** | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| Creating a publicly available German legal text corpus consisting of judgments that have been annotated by a legal | |
| expert. The annotated components consist of *conclusion*, *definition* and *subsumption* of the German legal writing | |
| style *Urteilsstil*. | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| *“The decision corpus is a collection of the decisions published on the website www.gesetze-bayern.de. At the time of | |
| the crawling the website offered 32,748 decisions of 131 Bavarian courts, dating back to 2015. The decisions are | |
| provided from the Bavarian state after the courts agreed to a publication. All decisions are processed by the publisher | |
| C.H.BECK, commissioned by the Bavarian state. This processing includes anonymisation, key-wording, and adding of | |
| editorial guidelines to the decisions.”* (Urchs. et al., 2021) | |
| #### Who are the source language producers? | |
| German courts from Bavaria | |
| ### Annotations | |
| #### Annotation process | |
| *“As stated above, the judgment corpus consist of 200 randomly chosen judgments that are annotated by a legal expert, | |
| who holds a first legal state exam. Due to financial, staff and time reasons the presented iteration of the corpus was | |
| only annotated by a single expert. In a future version several other experts will annotate the corpus and the | |
| inter-annotator agreement will be calculated.”* (Urchs. et al., 2021) | |
| #### Who are the annotators? | |
| A legal expert, who holds a first legal state exam. | |
| ### Personal and Sensitive Information | |
| *"All decisions are processed by the publisher C.H.BECK, commissioned by the Bavarian state. This processing includes ** | |
| anonymisation**, key-wording, and adding of editorial guidelines to the decisions.”* (Urchs. et al., 2021) | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| The SoMaJo Sentence Splitter has been used. Upon manual inspection of the dataset, we could see that the sentence | |
| splitter had poor accuracy in some cases (see ```analyze_dataset()``` in ```convert_to_hf_dataset.py```). When creating | |
| the splits, we thought about merging small sentences with their neighbors or removing them all together. However, since | |
| we could not find an straightforward way to do this, we decided to leave the dataset content untouched. | |
| Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton | |
| Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset | |
| consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the | |
| dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, | |
| differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to | |
| have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the | |
| original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to | |
| the bibliographical references and the original Github repositories and/or web pages provided in this dataset card. | |
| ## Additional Information | |
| ### Dataset Curators | |
| The names of the original dataset curators and creators can be found in references given below, in the section *Citation | |
| Information*. Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch) | |
| ; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch) | |
| ; [Github](https://github.com/kapllan)). | |
| ### Licensing Information | |
| [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) | |
| ### Citation Information | |
| ``` | |
| @dataset{urchs_stefanie_2020_3936490, | |
| author = {Urchs, Stefanie and | |
| Mitrović, Jelena}, | |
| title = {{German legal jugements annotated with judement | |
| style components}}, | |
| month = jul, | |
| year = 2020, | |
| publisher = {Zenodo}, | |
| doi = {10.5281/zenodo.3936490}, | |
| url = {https://doi.org/10.5281/zenodo.3936490} | |
| } | |
| ``` | |
| ``` | |
| @conference{icaart21, | |
| author = {Urchs., Stefanie and Mitrovi{\'{c}}., Jelena and Granitzer., Michael}, | |
| booktitle = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,}, | |
| doi = {10.5220/0010187305150521}, | |
| isbn = {978-989-758-484-8}, | |
| issn = {2184-433X}, | |
| organization = {INSTICC}, | |
| pages = {515--521}, | |
| publisher = {SciTePress}, | |
| title = {{Design and Implementation of German Legal Decision Corpora}}, | |
| year = {2021} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@kapllan](https://github.com/kapllan) and [@joelniklaus](https://github.com/joelniklaus) for adding this | |
| dataset. | |