# Dataset Card for Dataset Name

## Dataset Description

This dataset contains Key Figures with their properties from german tax acts. The dataset is annotated by tax experts and consists of 85 annotated paragraphs from 14 different German tax acts with 157 annotated tax key figures.
The annotation was performed based on a developed universally applicable annotation schema and a semantic model for key figures and their properties in legal texts.
More details about the schema and the semantic model can be found in our [Paper](https://ceur-ws.org/Vol-3441/paper7.pdf). There we also provide a knowledge graph populated from these annotated paragraphs.

- **Repository:** https://github.com/danielsteinigen/nlp-legal-texts
- **Paper:** https://ceur-ws.org/Vol-3441/paper7.pdf

### Supported Tasks and Leaderboards

- Entity Extraction
- Relation Extraction

### Languages

- German

## Dataset Structure

### Data Fields

- **id:** a unique ID of the data sample
- **law:** the abbreviation of the law from which this parapgraph originates
- **paragraph:** the title of the paragraph
- **text:** the actual text string of the paragraph
- **entities:** list of entity objects
- **relation:** list of relation objects

Entities:
- **id:** a unique ID of the entity
- **start:** start character offset of the entity
- **end:** end character offset of the entity
- **entity:** label/name of the entity
- **subclass:** entity subclass, if there is one, else NULL
- **text:** text string of the entity

Relations:
- **id:** a unique ID of the realation
- **head:** ID of the head entity
- **tail:** ID of the tail entity
- **relation:** label/name of the relation

## Dataset Creation

More details about the annotation process can be found in our [Paper](https://ceur-ws.org/Vol-3441/paper7.pdf).

## Additional Information

### Citation

**BibTeX:**
```
@inproceedings{steinigen2023semantic,
  title={Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models},
  author={Steinigen, Daniel and Namysl, Marcin and Hepperle, Markus and Krekeler, Jan and Landgraf, Susanne},
  url = {https://ceur-ws.org/Vol-3441/paper7.pdf},
  year={2023}
  journal={Sixth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2023)}, 
  series = {CEUR Workshop Proceedings},
  venue = {Braga, Portugal},
  eventdate = {2023-06-23}
}

```

**APA:**

Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models. 
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023. 
CEUR-WS.org, online CEUR-WS.org/Vol-3441/paper7.pdf.


### Licensing Information
```
MIT License

Copyright (c) 2023 Daniel Steinigen

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
task_categories:token-classification size_categories:n<1K language:de license:mit relation extraction entity extraction legal tax law region:us
{"language": ["de"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["token-classification"], "pretty_name": "Key Figures from german Tax Acts", "tags": ["relation extraction", "entity extraction", "legal", "tax law"]}
TAGS
#task_categories-token-classification #size_categories-n<1K #language-German #license-mit #relation extraction #entity extraction #legal #tax law #region-us 

# Dataset Card for Dataset Name

## Dataset Description

This dataset contains Key Figures with their properties from german tax acts. The dataset is annotated by tax experts and consists of 85 annotated paragraphs from 14 different German tax acts with 157 annotated tax key figures.
The annotation was performed based on a developed universally applicable annotation schema and a semantic model for key figures and their properties in legal texts.
More details about the schema and the semantic model can be found in our Paper. There we also provide a knowledge graph populated from these annotated paragraphs.

- Repository: URL
- Paper: URL

### Supported Tasks and Leaderboards

- Entity Extraction
- Relation Extraction

### Languages

- German

## Dataset Structure

### Data Fields

- id: a unique ID of the data sample
- law: the abbreviation of the law from which this parapgraph originates
- paragraph: the title of the paragraph
- text: the actual text string of the paragraph
- entities: list of entity objects
- relation: list of relation objects

Entities:
- id: a unique ID of the entity
- start: start character offset of the entity
- end: end character offset of the entity
- entity: label/name of the entity
- subclass: entity subclass, if there is one, else NULL
- text: text string of the entity

Relations:
- id: a unique ID of the realation
- head: ID of the head entity
- tail: ID of the tail entity
- relation: label/name of the relation

## Dataset Creation

More details about the annotation process can be found in our Paper.

## Additional Information

BibTeX:


APA:

Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models. 
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023. 
URL, online URL


### Licensing Information

# Dataset Card for Dataset Name ## Dataset Description

This dataset contains Key Figures with their properties from german tax acts. The dataset is annotated by tax experts and consists of 85 annotated paragraphs from 14 different German tax acts with 157 annotated tax key figures.
The annotation was performed based on a developed universally applicable annotation schema and a semantic model for key figures and their properties in legal texts.
More details about the schema and the semantic model can be found in our Paper. There we also provide a knowledge graph populated from these annotated paragraphs.

- Repository: URL
- Paper: URL ### Supported Tasks and Leaderboards

- Entity Extraction
- Relation Extraction ### Languages

- German ## Dataset Structure ### Data Fields

- id: a unique ID of the data sample
- law: the abbreviation of the law from which this parapgraph originates
- paragraph: the title of the paragraph
- text: the actual text string of the paragraph
- entities: list of entity objects
- relation: list of relation objects

Entities:
- id: a unique ID of the entity
- start: start character offset of the entity
- end: end character offset of the entity
- entity: label/name of the entity
- subclass: entity subclass, if there is one, else NULL
- text: text string of the entity

Relations:
- id: a unique ID of the realation
- head: ID of the head entity
- tail: ID of the tail entity
- relation: label/name of the relation ## Dataset Creation

More details about the annotation process can be found in our Paper. ## Additional Information

BibTeX:


APA:

Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models. 
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023. 
URL, online URL ### Licensing Information
TAGS
#task_categories-token-classification #size_categories-n<1K #language-German #license-mit #relation extraction #entity extraction #legal #tax law #region-us 
 # Dataset Card for Dataset Name ## Dataset Description

This dataset contains Key Figures with their properties from german tax acts. The dataset is annotated by tax experts and consists of 85 annotated paragraphs from 14 different German tax acts with 157 annotated tax key figures.
The annotation was performed based on a developed universally applicable annotation schema and a semantic model for key figures and their properties in legal texts.
More details about the schema and the semantic model can be found in our Paper. There we also provide a knowledge graph populated from these annotated paragraphs.

- Repository: URL
- Paper: URL ### Supported Tasks and Leaderboards

- Entity Extraction
- Relation Extraction ### Languages

- German ## Dataset Structure ### Data Fields

- id: a unique ID of the data sample
- law: the abbreviation of the law from which this parapgraph originates
- paragraph: the title of the paragraph
- text: the actual text string of the paragraph
- entities: list of entity objects
- relation: list of relation objects

Entities:
- id: a unique ID of the entity
- start: start character offset of the entity
- end: end character offset of the entity
- entity: label/name of the entity
- subclass: entity subclass, if there is one, else NULL
- text: text string of the entity

Relations:
- id: a unique ID of the realation
- head: ID of the head entity
- tail: ID of the tail entity
- relation: label/name of the relation ## Dataset Creation

More details about the annotation process can be found in our Paper. ## Additional Information

BibTeX:


APA:

Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models. 
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023. 
URL, online URL ### Licensing Information
passage: TAGS
#task_categories-token-classification #size_categories-n<1K #language-German #license-mit #relation extraction #entity extraction #legal #tax law #region-us 
# Dataset Card for Dataset Name## Dataset Description

This dataset contains Key Figures with their properties from german tax acts. The dataset is annotated by tax experts and consists of 85 annotated paragraphs from 14 different German tax acts with 157 annotated tax key figures.
The annotation was performed based on a developed universally applicable annotation schema and a semantic model for key figures and their properties in legal texts.
More details about the schema and the semantic model can be found in our Paper. There we also provide a knowledge graph populated from these annotated paragraphs.

- Repository: URL
- Paper: URL### Supported Tasks and Leaderboards

- Entity Extraction
- Relation Extraction### Languages

- German## Dataset Structure### Data Fields

- id: a unique ID of the data sample
- law: the abbreviation of the law from which this parapgraph originates
- paragraph: the title of the paragraph
- text: the actual text string of the paragraph
- entities: list of entity objects
- relation: list of relation objects

Entities:
- id: a unique ID of the entity
- start: start character offset of the entity
- end: end character offset of the entity
- entity: label/name of the entity
- subclass: entity subclass, if there is one, else NULL
- text: text string of the entity

Relations:
- id: a unique ID of the realation
- head: ID of the head entity
- tail: ID of the tail entity
- relation: label/name of the relation## Dataset Creation

More details about the annotation process can be found in our Paper.