README.md

---
license: cc-by-sa-4.0
task_categories:
- text-classification
- token-classification
language:
- pt
pretty_name: Puntuguese - A Corpus of Puns in Portuguese with Micro-editions
tags:
- humor
- puns
- humor-recognition
- pun-location
---

# Puntuguese - A Corpus of Puns in Portuguese with Micro-editions

Puntuguese is a corpus of punning texts in Portuguese, including jokes in Brazilian and European Portuguese. The data has been manually gathered and curate according to our [guidelines](data/GUIDELINES.md). It also contains some layers of annotation:

- Every pun is classified as homophonic, homographic, both, or none according to their specific punning signs;
- The punning and alternative signs were made explicit for every joke;
- We also mark potentially problematic puns from an ethical perspective, so it is easier to filter them out if needed.

Additionally, every joke in the corpus has a non-humorous counterpart, obtained via micro-editing, to enable Machine Learning systems to be trained.

### Dataset Description

- **Curated by:** [Marcio Lima Inácio](https://eden.dei.uc.pt/~mlinacio/)
- **Funded by:** FCT - Foundation for Science and Technology, I.P. (grant number UI/BD/153496/2022) and the Portuguese Recovery and Resilience Plan (project C645008882-00000055, Center for Responsible AI).
- **Languages:** Brazilian Portuguese; European Portuguese
- **License:** CC-BY-SA-4.0
  
### Dataset Sources

The puns were collected from three sources: the "Maiores e melhores" web blog, the "O Sagrado Caderno das Piadas Secas" instagram page, and from the "UTC - Ultimate Trocadilho Challenge" by Castro Brothers on Youtube.

- **Repository:** https://github.com/Superar/Puntuguese
- **Paper:** To be announced

## Dataset Structure

The dataset provided via Hugging Face Hub contains two tasks: humor recognition and pun location. The first task uses the `text` and `label` columns. For pun location, the columns to be used are `tokens` and `labels`. An instance example can be seen below:

```json
{
 "id": "1.1.H",
 "text": "Deve ser difícil ser professor de natação. Você ensina, ensina, e o aluno nada.",
 "label": 1,
 "tokens": ["Deve", "ser", "difícil", "ser", "professor", "de", "natação", ".", "Você", "ensina", ",", "ensina", ",", "e", "o", "aluno", "nada", "."],
 "labels": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
}
```

## Dataset Creation

#### Data Collection and Processing

The data was manually gathered and curated to ensure that all jokes followed our chosen definition of pun by Miller et al. (2017):

> "A pun is a form of wordplay in which one sign (e.g., a word or phrase) suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another sign, for an intended humorous or rhetorical effect."

Every selected pun must satisfy this definition. Gatherers were also provided some hints for this process:

- A sign can be a single word (or token), a phrase (a sequence of tokens), or a part of a word (a subtoken);
- The humorous effect must rely on the ambiguity of said sign;
- The ambiguity must originate from the word's form (written or spoken);
- Every pun must have a "pun word" (the ambiguous sign that is in the text) and an "alternative word" (the sign's ambiguous interpretation) identified. If it is not possible to identify both, the text is not considered a pun and should not be included.

#### Who are the source data producers?

The original data was produced by professional comedians from the mentioned sources.

## Bias, Risks, and Limitations

As in every real-life scenario, the data can contain problematic and unsensitive jokes about delicate subejcts. In this sense, we provide in out GitHub repository a list of jokes that the gatherers, personally, thought to be problematic.

## Citation

**BibTeX:**

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to be defined
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**APA:**

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to be defined
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