Automatically Categorizing Written Texts by Author Gender

By Moshe Koppel, Shlomo Engelson Argamon, Anat Rachel Shimoni. 2002

775 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “For example, how are social groups delineated when defining demographic attribute prediction tasks (e.g., Koppel et al., 2002; Rosenthal and McKeown, 2011; Nguyen et al., 2013)?”
Multi-Dimensional Gender Bias Classification (2020, background): “More specifically, automatic gender attribution has reported many successes (Koppel et al., 2002; Koolen and van Cranenburgh, 2017; Qian, 2019), often driven by the fact that authors of specific genders tend to prefer producing content about topics that belie those gender (Sarawgi et al.”
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting (2019, background): “Computational linguistics researchers have explored the use of lexical and syntactic features to infer authors’ genders [15, 30].”
Adversarial Removal of Demographic Attributes from Text Data (2018, background): “It is well established that textual information can be predictive of age, race, gender, and many other social factors of the author (Koppel et al., 2002; Burger et al., 2011; Nguyen et al., 2013; Weren et al., 2014; Verhoeven and Daelemans, 2014; Rangel et al., 2016; Verhoeven et al., 2016; Blodgett”
Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs (2011, background): “…1984; Juola, 2008), personality-type classification (Argamon et al., 2005; Oberlander and Gill, 2006; Mairesse et al., 2007), gender categorization (Koppel et al., 2002; Mukherjee and Liu, 2010; Herring and Paolillo, 2006), identification of interactional style (Jurafsky et al., 2009; Ranganath…”
Gender as a Variable in Natural-Language Processing: Ethical Considerations (2017, background): “…should consider the following spe-ciﬁc recommendations: First, if a study relies upon a gender-category ascription provided by someone else, as does Koppel et al. (2002), it should provide as much information as possible about how the category was ascribed and acknowledge the third-party category…”
Demographic Factors Improve Classification Performance (2015, methodology): “It is often used in author proﬁling (Koppel et al., 2002) and stylometrics (Goswami et al., 2009; Sarawgi et al., 2011).”
A New Algorithmic Identity (2011, methodology): “…works of women and men, statistical correlations were run to create a vector-based categorization of two gender’s written features, through which the algorithm was able to then identify the ‘correct’ gender of a new, non-categorized work with a success rate of 80 percent (Koppel et al., 2002).”
Personalized Machine Translation: Preserving Original Author Traits (2016, background): “1% accuracy for English originals, in line with accuracies reported in the literature (Koppel et al., 2002).”
The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language (2009, methodology): “To answer this question, we use three different data sets that we construct for this purpose – consisting of true and false short statements on three different topics – and attempt to automatically separate them using standard natural language processing techniques.”
Computational methods in authorship attribution (2009, positive): “…words) as a simple method for approximating syntactic features for this purpose (Argamon-Engelson et al. 1998; Kukushkina et al. 2001; De Vel et al. 2001; Koppel et al. 2002; Koppel & Schler 2003; Chaski 2005; Koppel et al. 2005, 2006a; van Halteren et al 2005; Zhao et al. 2006; Zheng et al. 2006).”
Reusable workflows for gender prediction (2018, background): “The earliest attempts at AP that covered gender identification started with Koppel et al. (2002), who used parts of the BNC, but continued on other corpora, such as the ACL corpus of scientific papers (Vogel and Jurafsky, 2012) or UGC corpora, esp.”