Studying the ways in which language is gendered has long been an area of
interest in sociolinguistics. 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.

本文介绍了一种生成潜在变量模型，用于量化形容词（或动词）选用及其情感，并以头（或依赖）名词的自然性别为条件。研究发现男女名词描述之间存在显著差异，并且这些差异与常见的性别刻板印象相一致：用于描述女性的积极形容词更与其身体相关而不是用于描述男性的形容词。