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@@ -45,6 +45,8 @@ This dataset distinguishes between different types of targets:
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  *IMPORTANT NOTE:* this dataset has been used to train a machine learning model, and **is not a list of all climate targets published by national governments**.
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  ## Dataset Description
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  This dataset includes 2,610 text passages containing 1,193 target mentions annotated in a multilabel setting: one text passage can be assigned to 0 or more target types. This breaks down as follows.
@@ -70,14 +72,23 @@ Please read our [Terms of Use](https://app.climatepolicyradar.org/terms-of-use),
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  ## Links
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- <!-- Provide the basic links for the dataset. -->
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- - **Repository:** [coming soon]
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- - **Paper** [coming soon]
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  ## Citation
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- [Coming soon]
 
 
 
 
 
 
 
 
 
 
 
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  ## Authors & Contact
 
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  *IMPORTANT NOTE:* this dataset has been used to train a machine learning model, and **is not a list of all climate targets published by national governments**.
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+ For more information on dataset creation, [see our paper](https://arxiv.org/abs/2404.02822).
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  ## Dataset Description
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  This dataset includes 2,610 text passages containing 1,193 target mentions annotated in a multilabel setting: one text passage can be assigned to 0 or more target types. This breaks down as follows.
 
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  ## Links
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+ - [Paper](https://arxiv.org/abs/2404.02822)
 
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  ## Citation
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+ *Juhasz, M., Marchand, T., Melwani, R., Dutia, K., Goodenough, S., Pim, H., & Franks, H. (2024). Identifying Climate Targets in National Laws and Policies using Machine Learning. arXiv preprint arXiv:2404.02822.*
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+ ```
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+ @misc{juhasz2024identifying,
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+ title={Identifying Climate Targets in National Laws and Policies using Machine Learning},
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+ author={Matyas Juhasz and Tina Marchand and Roshan Melwani and Kalyan Dutia and Sarah Goodenough and Harrison Pim and Henry Franks},
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+ year={2024},
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+ eprint={2404.02822},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CY}
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+ }
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+ ```
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  ## Authors & Contact