arimuti/pejorativity

Dataset Card for Dataset Name

PejorativITy is a corpus of 1,200 expert-annotated Italian tweets for pejorative language at the word level and misogyny at sentence level.

Dataset Details

Dataset Description

Misogyny is often expressed through figurative language. Some neutral words can assume a negative connotation when functioning as pejorative epithets. Disambiguating the meaning of such terms might help the detection of misogyny. In order to address such task, we present PejorativITy, a novel corpus of 1,200 manually annotated Italian tweets for pejorative language at the word level and misogyny at the sentence level.

• Curated by: Arianna Muti & Federico Ruggeri
• Language(s) (NLP): Italian

Dataset Sources

• Repository: https://github.com/arimuti/PejorativITy
• Paper: https://aclanthology.org/2024.lrec-main.1112/

BibTeX:

@inproceedings{muti-etal-2024-pejorativity-disambiguating, title = "{P}ejorativ{IT}y: Disambiguating Pejorative Epithets to Improve Misogyny Detection in {I}talian Tweets", author = "Muti, Arianna and Ruggeri, Federico and Toraman, Cagri and Barr{'o}n-Cede{~n}o, Alberto and Algherini, Samuel and Musetti, Lorenzo and Ronchi, Silvia and Saretto, Gianmarco and Zapparoli, Caterina", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.1112", pages = "12700--12711", abstract = "Misogyny is often expressed through figurative language. Some neutral words can assume a negative connotation when functioning as pejorative epithets. Disambiguating the meaning of such terms might help the detection of misogyny. In order to address such task, we present PejorativITy, a novel corpus of 1,200 manually annotated Italian tweets for pejorative language at the word level and misogyny at the sentence level. We evaluate the impact of injecting information about disambiguated words into a model targeting misogyny detection. In particular, we explore two different approaches for injection: concatenation of pejorative information and substitution of ambiguous words with univocal terms. Our experimental results, both on our corpus and on two popular benchmarks on Italian tweets, show that both approaches lead to a major classification improvement, indicating that word sense disambiguation is a promising preliminary step for misogyny detection. Furthermore, we investigate LLMs{'} understanding of pejorative epithets by means of contextual word embeddings analysis and prompting.", }

Dataset Card Contact

Arianna Muti: arianna.muti2@unibo.it

Federico Ruggeri: federico.ruggeri6@unibo.it