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.

翻译：厌女情绪常通过比喻性语言表达。某些中性词汇在充当贬义修饰语时会具有负面含义。解歧此类词汇的语义有助于检测厌女内容。为此，我们构建了PejorativITy语料库，包含1,200条人工标注的意大利语推文，在词汇层面标注贬义语言，在句子层面标注厌女内容。我们评估了将消歧词义信息注入厌女检测模型的效果，并探索了两种信息注入方法：贬义信息拼接与歧义词替换为单义术语。实验结果表明，在自建语料库及两个意大利推文基准数据集上，两种方法均显著提升分类性能，说明词义消歧是厌女检测的有前景的预处理步骤。此外，我们通过上下文词嵌入分析与提示工程探究了大语言模型对贬义修饰语的理解能力。