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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- es |
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pretty_name: AbstRCT-ES |
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--- |
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--- |
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dataset_info: |
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- config_name: es |
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data_files: |
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- split: neoplasm_train |
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path: es/neoplasm_train-* |
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- split: neoplasm_dev |
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path: es/neoplasm_dev-* |
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- split: neoplasm_test |
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path: es/neoplasm_test-* |
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- split: glaucoma_test |
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path: es/glaucoma_test-* |
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- split: mixed_test |
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path: es/mixed_test-* |
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license: apache-2.0 |
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task_categories: |
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- token-classification |
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language: |
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- es |
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tags: |
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- biology |
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- medical |
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pretty_name: AbstRCT-ES |
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--- |
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<p align="center"> |
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<br> |
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<img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="width: 30%;"> |
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<h2 align="center">AbstRCT-ES</h2> |
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<be> |
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We translate the [AbstRCT English Argument Mining Dataset](https://gitlab.com/tomaye/abstrct) to generate a parallel Spanish version |
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using DeepL; labels are projected using [Easy Label Projection](https://github.com/ikergarcia1996/Easy-Label-Projection) and manually corrected. |
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|
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- 📖 Paper: [Crosslingual Argument Mining in the Medical Domain](https://arxiv.org/abs/2301.10527) |
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- 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote) |
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- Code: [https://github.com/ragerri/abstrct-projections/tree/final](https://github.com/ragerri/abstrct-projections/tree/final) |
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- Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR |
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## Labels |
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```python |
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{ |
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"O": 0, |
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"B-Claim": 1, |
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"I-Claim": 2, |
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"B-Premise": 3, |
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"I-Premise": 4, |
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} |
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``` |
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A `claim` is a concluding statement made by the author about the outcome of the study. In the medical domain it may be an assertion of a diagnosis or a treatment. |
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A `premise` corresponds to an observation or measurement in the study (ground truth), which supports or attacks another argument component, usually a claim. |
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It is important that they are observed facts, therefore, credible without further evidence. |
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## Citation |
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````bibtex |
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@misc{yeginbergen2024crosslingual, |
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title={Cross-lingual Argument Mining in the Medical Domain}, |
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author={Anar Yeginbergen and Rodrigo Agerri}, |
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year={2024}, |
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eprint={2301.10527}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
