PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets

Date

2024-01-01

Author

Muti, Arianna
Ruggeri, Federico
Toraman, Çağrı
Musetti, Lorenzo
Algherini, Samuel
Ronchi, Silvia
Saretto, Gianmarco
Zapparoli, Caterina
Barrón-Cedeño, Alberto

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Hate speech detection, Pejorative language, Word sense disambiguation

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195928579&origin=inward
https://hdl.handle.net/11511/110285

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Department of Computer Engineering, Conference / Seminar