Dataset Card for environmental_claims
17
25 commits
1 linked in READMEs
updated May 23, 2023
We introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies.
The dataset supports a binary classification task of whether a given sentence is an environmental claim or not.
The text in the dataset is in English.
{
"text": "It will enable E.ON to acquire and leverage a comprehensive understanding of the transfor- mation of the energy system and the interplay between the individual submarkets in regional and local energy supply sys- tems.",
"label": 0
}
The dataset is split into:
[More Information Needed]
Our dataset contains environmental claims by firms, often in the financial domain. We collect text from corporate annual reports, sustainability reports, and earning calls transcripts.
For more information regarding our sample selection, please refer to Appendix B of our paper, which is provided for citation.
Mainly large listed companies.
For more information on our annotation process and annotation guidelines, please refer to Appendix C of our paper, which is provided for citation.
The authors and students at University of Zurich with majors in finance and sustainable finance.
Since our text sources contain public information, no personal and sensitive information should be included.
[More Information Needed]
[More Information Needed]
[More Information Needed]
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit creativecommons.org/licenses/by-nc-sa/4.0.
If you are interested in commercial use of the dataset, please contact [email protected].
@misc{stammbach2022environmentalclaims,
title = {A Dataset for Detecting Real-World Environmental Claims},
author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus},
year = {2022},
doi = {10.48550/ARXIV.2209.00507},
url = {https://arxiv.org/abs/2209.00507},
publisher = {arXiv},
}
Thanks to @webersni for adding this dataset.
Dataset Card for environmental_claims
17
25 commits
1 linked in READMEs
updated May 23, 2023
We introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies.
The dataset supports a binary classification task of whether a given sentence is an environmental claim or not.
The text in the dataset is in English.
{
"text": "It will enable E.ON to acquire and leverage a comprehensive understanding of the transfor- mation of the energy system and the interplay between the individual submarkets in regional and local energy supply sys- tems.",
"label": 0
}
The dataset is split into:
[More Information Needed]
Our dataset contains environmental claims by firms, often in the financial domain. We collect text from corporate annual reports, sustainability reports, and earning calls transcripts.
For more information regarding our sample selection, please refer to Appendix B of our paper, which is provided for citation.
Mainly large listed companies.
For more information on our annotation process and annotation guidelines, please refer to Appendix C of our paper, which is provided for citation.
The authors and students at University of Zurich with majors in finance and sustainable finance.
Since our text sources contain public information, no personal and sensitive information should be included.
[More Information Needed]
[More Information Needed]
[More Information Needed]
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit creativecommons.org/licenses/by-nc-sa/4.0.
If you are interested in commercial use of the dataset, please contact [email protected].
@misc{stammbach2022environmentalclaims,
title = {A Dataset for Detecting Real-World Environmental Claims},
author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus},
year = {2022},
doi = {10.48550/ARXIV.2209.00507},
url = {https://arxiv.org/abs/2209.00507},
publisher = {arXiv},
}
Thanks to @webersni for adding this dataset.