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Unsupervised Discovery of Gendered Language through Latent-Variable Modeling

机译:通过潜在变量建模无监督地发现性别语言

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Studying the ways in which language is gendered has long been an area of interest in so-ciolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men.
机译:长期以来,研究语言性别的方式一直是社会语言学领域的研究热点。例如,研究探索了电影中男女角色的讲话以及用来描述男女政客的语言。在本文中,我们的目标不仅仅是定性地研究这种现象,而是量化用于描述男人和女人的语言在多大程度上有所不同,而且在正面或负面方面也有所不同。为此,我们引入了一个生成的潜在变量模型,该模型共同表示形容词(或动词)选择及其情感,并给出了头(或从属)名词的自然性别。我们发现,男性和女性名词的描述之间存在显着差异,并且这些差异与常见的性别刻板印象一致:用于描述女性的肯定形容词比用于描述男性的形容词更多地与她们的身体有关。

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