README.md

---
license: cc-by-nc-sa-4.0
language:
- es
pretty_name: 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
---

<p align="center">
    <br>
    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="width: 30%;">
    <h2 align="center">AbstRCT-ES</h2>
    <be>


We translate the [AbstRCT English Argument Mining Dataset](https://gitlab.com/tomaye/abstrct) to generate a parallel Spanish version
using DeepL; labels are projected using [Easy Label Projection](https://github.com/ikergarcia1996/Easy-Label-Projection) and manually corrected.

  - 📖 Paper: [Crosslingual Argument Mining in the Medical Domain](https://arxiv.org/abs/2301.10527)
  - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
  - Code: [https://github.com/ragerri/abstrct-projections/tree/final](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
```python
{
"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

````bibtex
@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}
}
````