license: mit
task_categories:
- token-classification
language:
- de
tags:
- relation extraction
- entity extraction
- legal
- tax law
pretty_name: Key Figures from german Tax Acts
size_categories:
- n<1K
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: 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.
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
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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
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.