Automatically Inferring Gender Associations from Language

By Serina Chang, K. McKeown. 2019

12 citations recorded by the source corpus.

In this paper, we pose the question: do people talk about women and men in different ways? We introduce two datasets and a novel integration of approaches for automatically inferring gender associations from language, discovering coherent word clusters, and labeling the clusters for the semantic concepts they represent. The datasets allow us to compare how people write about women and men in two different settings – one set draws from celebrity news and the other from student reviews of computer science professors. We demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. Human evaluations show that our methods significantly outperform strong baselines.

Subfields: Artificial Intelligence, Gender Studies, Natural Language Processing

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Citations in the corpus

Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “…systems (Sun et al., 2019), uncovering many gender disparities and harmful biases in algorithms and text (Cao and Daum´e III 2020; Chang et al. 2019; Chang and McKeown 2019; Costa-juss ` a 2019; Du et al. 2019; Emami et al. 2019; Garimella et al. 2019; Gaut et al. 2020; Habash et al. 2019;…”
Theories of “Gender” in NLP Bias Research (2022, unknown): “418 [8] Serina Chang and Kathleen McKeown.”
Adhering, Steering, and Queering: Treatment of Gender in Natural Language Generation (2020, methodology): “For example, a widely used approach is to apply a predictive classiﬁer, such as logistic regression, to predict gender in input texts [3,51], or to use statistical tests to ﬁnd gender associations [12].”