Detecting Gender Stereotypes: Lexicon vs. Supervised Learning Methods
Jenna Cryan, Shiliang Tang, Xinyi Zhang, Miriam J. Metzger, Hai-Tao Zheng, Ben Y. Zhao
Abstract
Biases in language influence how we interact with each other and society at large. Language affirming gender stereotypes is often observed in various contexts today, from recommendation letters and Wikipedia entries to fiction novels and movie dialogue. Yet to date, there is little agreement on the methodology to quantify gender stereotypes in natural language (specifically the English language). Common methodology (including those adopted by companies tasked with detecting gender bias) rely on a lexicon approach largely based on the original BSRI study from 1974.
Topics & Concepts
LexiconGender biasComputer scienceNatural language processingArtificial intelligenceNatural languageNatural (archaeology)LinguisticsPsychologySocial psychologyHistoryPhilosophyArchaeologyHate Speech and Cyberbullying DetectionGender Studies in LanguageMedia Influence and Politics