HiTZ/AbstRCT-ES

---

dataset_info: - config_name: es data_files: - split: neoplasm_train path: es/neoplasm_train-* - split: neoplasm_dev path: es/neoplasm_dev-* - split: neoplasm_test path: es/neoplasm_test-* - split: glaucoma_test path: es/glaucoma_test-* - split: mixed_test path: es/mixed_test-* license: apache-2.0 task_categories: - token-classification language: - es tags: - biology - medical pretty_name: AbstRCT-ES

AbstRCT-ES

We translate the AbstRCT English Argument Mining Dataset to generate a parallel Spanish version using DeepL; labels are projected using Easy Label Projection and manually corrected.

• 📖 Paper: Crosslingual Argument Mining in the Medical Domain
• 🌐 Project Website: https://univ-cotedazur.eu/antidote
• Code: https://github.com/ragerri/abstrct-projections/tree/final
• Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR

Labels

{
"O": 0,
"B-Claim": 1,
"I-Claim": 2,
"B-Premise": 3,
"I-Premise": 4,
}

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. A premise corresponds to an observation or measurement in the study (ground truth), which supports or attacks another argument component, usually a claim. It is important that they are observed facts, therefore, credible without further evidence.

Citation

@misc{yeginbergen2024crosslingual,
      title={Cross-lingual Argument Mining in the Medical Domain}, 
      author={Anar Yeginbergen and Rodrigo Agerri},
      year={2024},
      eprint={2301.10527},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}