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.

揭示了使用意象语言表达的厌女情绪，分析中性词汇负面含义与歧视女性的关系，提出了一个基于推文语料库的方法，在词级别上注释了 1,200 个意大利推文中的贬义词汇以及句子级别上的厌女情绪，并研究了注入需解释词汇信息对厌女情绪检测模型的作用和两种注入方法的效果，结果表明词义消歧是厌女情绪检测的一个有前景的初步步骤。此外，通过上下文词嵌入分析和提示，对贬义词汇的理解进行了研究。