What's in a name?

By R. de Levie. 1990

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In Linguistics

Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “…(2019); Font and Costa-jussà (2019); James-Sorenson and Alvarez-Melis (2019); Kurita et al. (2019); Mayfield et al. (2019); Pujari et al. (2019); Romanov et al. (2019); Ruane et al. (2019); Sedoc and Ungar (2019); Sun et al. (2019); Zmigrod et al. (2019); Hutchinson et al. (2020);…”
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2019, background): “Romanov et al. (2019) generalized this work to a multi-class setting, exploring methods to mitigate bias in an occupation classification task.”
Measuring and Reducing Gendered Correlations in Pre-trained Models (2020, neutral): “Racial and gender bias has been documented in resume-job matching software (De-Arteaga et al., 2019; Romanov et al., 2019), sentiment analysis (Kiritchenko and Mohammad, 2018), coreference resolution (Rudinger et al., 2018; Zhao et al., 2018a; Webster et al., 2018), image captioning (Zhao et al.,…”
Towards Understanding Gender Bias in Relation Extraction (2019, background): “Existing studies have revealed gender bias in various NLP tasks (Zhao et al., 2017; Rudinger et al., 2018; Zhao et al., 2018; Dixon et al., 2018; Lu et al., 2018; Kir-itchenko and Mohammad, 2018; Romanov et al., 2019; Sheng et al., 2019; Sun et al., 2019).”
To Be or Not Be? The Influence of Dissociative Reference Groups on Consumer Preferences (2006, background): “…tactics (such as self-handicapping; Sheppard & Arkin, 1989), is positively correlated with favoring national brand labels over bargain brand labels (Bushman, 1993), is associated with observed and self-reported cosmetic use (Miller & Cox, 1982), and is related to the use of clothing to present a…”
Understanding Social Media Disclosures of Sexual Abuse Through the Lenses of Support Seeking and Anonymity (2016, background): “According to sociologist Gary Marx, anonymity “means that a person cannot be identified according to any of seven dimensions of identity knowledge,” including legal name, location, behavior patterns, social group membership, identifying personal characteristics, or pseudonyms that can be linked with”
Biodiversity loss and the taxonomic bottleneck: emerging biodiversity science (2006, background): “Taxonomy (or systematics) is the fundamental discipline of biology dedicated to the description, naming, and cataloging of organisms and their relationships (Mayr and Ashlock 1991; Knapp 2000; Wheeler 2004; Wheeler et al. 2004).”
More than meets the eye: The influence of implicit and explicit self-esteem on materialism (2011, background): “Indeed, it has been shown that individuals with depleted cognitive capacity report explicit selfesteem corresponding more closely to levels of implicit selfesteem than do individuals with intact cognitive capacity (Koole et al., 2001).”
Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “…resolution, including, for instance, natural language inference (Rudinger, May, and Van Durme 2017), word embeddings (e.g., Bolukbasi et al. 2016; Romanov et al. 2019; Gonen and Goldberg 2019), sentiment analysis (Kiritchenko and Mohammad 2018), machine translation (Font and Costa-jussà 2019;…”
The danger of misinterpreting short-window event study findings in strategic management research: an empirical illustration using horizontal acquisitions (2008, background): “…0 Merchant and International joint ventures 0 to 1 Schendel (2000) Davidson and Changes in executive 5 to 1, 1 to 0 Nemec (2001) duality/plurality Lee (2001) Adding .com to a company name 1 to 1 APPENDIX (Continued) Paper Brief description of Window(s) beginning primary event studied and end…”
Gender Inference of Twitter Users in Non-English Contexts (2013, methodology): “We used Amazon Mechanical Turk to manually label each user with their gender, using a language-agnostic labeling strategy (Liu and Ruths, 2013).”
Who Wants a Good Reputation (2001, background): “Section 4 explains why we ignore these equilibria. competent firms always choose high effort, gradually building and then maintaining a reputation for competency and high effort.8 In a complementary paper, Tadelis (1999) studies the market for firm names in an adverse selection environment.”