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Word embedding spaces are powerful tools for capturing latent semantic relationships between terms in corpora, and have become widely popular for building state-of-the-art natural language processing algorithms.
“Efficient Estimation of Word Representations in Vector Space”, 2013
Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean · 2013
Earlier work this paper cites.
“Glove: Global Vectors for Word Representation”
Jeffrey Pennington, Richard Socher and Christopher Manning · 2014
Earlier work this paper cites.
“Man Is to Computer Programmer as Woman Is to Homemaker? Debiasing Word Embeddings”
Tolga Bolukbasi et al · 2016
Earlier work this paper cites.
“Semantics Derived Automatically from Language Corpora Contain Human-like Biases”
Aylin Caliskan, Joanna. Bryson and Arvind Narayanan · 2017
Cited alongside, same era.
“Evaluating the Stability of Embedding-Based Word Similarities”
Maria Antoniak and David Mimno · 2018
Cited alongside, same era.
“Understanding the Origins of Bias in Word Embeddings”, 2018
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson and Richard Zemel · 2018
Later among the works it cites.
“Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes”
Nikhil Garg, Londa Schiebinger, Dan Jurafsky and James Zou · 2018
Later among the works it cites.
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