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Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP).
Wordnet: a lexical database for english
George A Miller · 1995
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Measuring individual differences in implicit cognition: the implicit association test
Anthony G Greenwald, Debbie E McGhee, and Jordan LK Schwartz · 1998
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Linguistic inquiry and word count: Liwc 2001
James W Pennebaker, Martha E Francis, and Roger J Booth · 2001
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English gigaword fifth edition ldc2011t07. dvd
Robert Parker, David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda · 2011
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Research methods in the age of digital journalism: Massive-scale automated analysis of news-content—topics, style and gender
Ilias Flaounas, Omar Ali, Thomas Lansdall-Welfare, Tijl De Bie, Nick Mosdell, Justin Lewis, and Nello Cristianini · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
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False positives, false negatives, and false analyses: A rejoinder to machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Anthony W Flores, Kristin Bechtel, and Christopher T Lowenkamp · 2016
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Women are seen more than heard in online newspapers
Sen Jia, Thomas Lansdall-Welfare, Saatviga Sudhahar, Cynthia Carter, and Nello Cristianini · 2016
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Semeval-2016 task 4: Sentiment analysis in twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Content analysis of 150 years of british periodicals
Thomas Lansdall-Welfare, Saatviga Sudhahar, James Thompson, Justin Lewis, FindMyPast Newspaper Team, Nello Cristianini, Amy Gregor, Boon Low, Toby Atkin-Wright, Malcolm Dobson, et al · 2017
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Statistical bulletin: Annual survey of hours and earnings: 2017 provisional and 2016 revised results, 2017
Office for National Statistics · 2017
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
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Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia · 2017
Cited alongside, same era.
Freudian slips: Analysing the internal representations of a neural network from its mistakes
Sen Jia, Thomas Lansdall-Welfare, and Nello Cristianini · 2017
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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
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Activis: Visual exploration of industry-scale deep neural network models
Minsuk Kahng, Pierre Y Andrews, Aditya Kalro, and Duen Horng Polo Chau · 2018
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