  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.
