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PtBrVId
PtBrVId is a Portuguese Variety Identification corpus, built by combining pre-existing datasets originally created for different NLP tasks and released under permissive licenses.
Our goal is to provide a large, diverse, and multi-domain resource for studying and improving automatic identification of European Portuguese (PT-PT) and Brazilian Portuguese (PT-BR).
📚 Data Sources
The corpus is composed of datasets from various domains, each selected to ensure (as much as possible) mono-variety content. The current release is silver-labeled and unsupervised, meaning that we cannot fully guarantee that all documents are strictly mono-variety. A future version will include a refined annotation schema with both automatic and manual verification.
| Domain | Variety | Dataset | Original Task | # Docs | License | Silver Labeled |
|---|---|---|---|---|---|---|
| Literature | PT-PT | Arquivo Pessoa | - | ~4k | CC | ✔ |
| Gutenberg Project | - | 6 | CC | ✔ | ||
| LT-Corpus | - | 56 | ELRA END USER | ✘ | ||
| PT-BR | Brazilian Literature | Author Identification | 81 | CC | ✘ | |
| LT-Corpus | - | 8 | ELRA END USER | ✘ | ||
| Politics | PT-PT | Koehn (2005) Europarl | Machine Translation | ~10k | CC | ✘ |
| PT-BR | Brazilian Senate Speeches1 | - | ~5k | CC | ✔ | |
| Journalistic | PT-PT | CETEM Público | - | 1M | CC | ✘ |
| PT-BR | CETEM Folha | - | 272k | CC | ✘ | |
| Social Media | PT-PT | Ramalho (2021) | Fake News Detection | 2M | MIT | ✔ |
| PT-BR | Vargas (2022) | Hate Speech Detection | 5k | CC-BY-NC-4.0 | ✘ | |
| Cunha (2021) | Fake News Detection | 2k | GPL-3.0 | ✔ | ||
| Web | BOTH | Ortiz-Suarez (2020) | - | 10k | CC | ✔ |
Table 1: PtBrVId data sources and metadata.
1 The Brazilian Senate Speeches dataset was created by the authors through web crawling of the Brazilian Senate website and is available on Hugging Face.
A raw version of the dataset is available here.
🛠 Annotation & Preprocessing
Annotation
We selected data sources known to contain primarily mono-variety Portuguese texts. While this approach helps ensure quality, this first release is entirely unsupervised. A planned v2 will introduce a hybrid annotation strategy combining automated labeling and manual review.
Preprocessing Pipeline
To standardize and clean the data, we applied the following steps:
- Remove NaN values.
- Remove empty documents.
- Remove duplicate documents.
- Apply the
clean-textlibrary to strip non-relevant content for variety identification. - Remove outlier documents with lengths below
Q1 - 1.5 × IQRor aboveQ3 + 1.5 × IQR, whereQ1andQ3are the first and third quartiles, andIQRis the interquartile range.
📖 Citation
If you use this corpus, please cite:
@article{Sousa_Almeida_Silvano_Cantante_Campos_Jorge_2025,
title={Enhancing Portuguese Variety Identification with Cross-Domain Approaches},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={39},
number={24},
pages={25192--25200},
year={2025},
doi={10.1609/aaai.v39i24.34705},
author={Sousa, Hugo and Almeida, Rúben and Silvano, Purificação and Cantante, Inês and Campos, Ricardo and Jorge, Alípio}
}