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The power of word embeddings is attributed to the linguistic theory that similar words will appear in similar contexts.
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Malinowski, Firth, and the “context of situation”
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Noun classification from predicate-argument structures
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Hinrich Schütze. 1992 · 1992
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Introduction to the Special Issue on Computational Linguistics Using Large Corpora
Kenneth W. Church and Robert L. Mercer. 1993 · 1993
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A minimalist program for linguistics: The work of Zellig Harris on meaning and information
Bruce E. Nevin. 1993 · 1993
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A vector model for syntagmatic and paradigmatic relatedness
Hinrich Schütze and Jan Pedersen. 1993 · 1993
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Computational linguistics and deep learning
Christopher D. Manning. 2015 · 2015
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Angela Senis. 2015 · 2015
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Marco Baroni. 2016 · 2016
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Deep learning
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Diachronic word embeddings reveal statistical laws of semantic change
William L. Hamilton, Jure Leskovec, and Dan Jurafsky. 2016 · 2016
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A persona-based neural conversation model
Jiwei Li, Michel Galley, Chris Brockett, Georgios P Spithourakis, Jianfeng Gao, and Bill Dolan. 2016 · 2016
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Word space
Hinrich Schütze. 1993 · 1993
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From context to contextualization
Peter Auer. 1996 · 1996
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A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge
Thomas K. Landauer and Susan T. Dumais. 1997 · 1997
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The contribution of John Rupert Firth to linguistics in the first fifty years of Lingua
R. H. Robins. 1997 · 1997
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From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model
Curt Burgess. 1998 · 1998
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Probabilistic latent semantic indexing
Thomas Hofmann. 1999 · 1999
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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Demographic-aware word associations
Aparna Garimella, Carmen Banea, and Rada Mihalcea. 2017 · 2017
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Neural network models for natural language processing
Yoav Goldberg. 2017 · 2017
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Attention is all you need
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Word embeddings quantify 100 years of gender and ethnic stereotypes
Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2018 · 2018
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Real-time personalization using embeddings for search ranking at airbnb
Mihajlo Grbovic and Haibin Cheng. 2018 · 2018
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The social and the neural network: How to make natural language processing about people again
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Diachronic word embeddings and semantic shifts: a survey
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Deep contextualized word representations
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The #BenderRule: On naming the languages we study and why it matters
Emily M. Bender. 2019 · 2019
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Firth and the origins of systemic functional linguistics: Process, pragma, and polysystem
David G. Butt. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Introduction to natural language processing
Jacob Eisenstein. 2019 · 2019
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The geometry of culture: Analyzing the meanings of class through word embeddings
Austin C. Kozlowski, Matt Taddy, and James A. Evans. 2019 · 2019
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Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R Bowman, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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Climbing towards NLU: On meaning, form, and understanding in the Age of Data
Emily M. Bender and Alexander Koller. 2020 · 2020
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Experience grounds language
Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, and Joseph Turian. 2020 · 2020
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Language (technology) is power: A critical survey of "bias" in NLP
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A Survey of the State of Explainable AI for Natural Language Processing
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Enriching Word Embeddings with Temporal and Spatial Information
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The unstoppable rise of computational linguistics in deep learning
James Henderson. 2020 · 2020
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Gender bias in neural natural language processing
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Leveraging contextual embeddings for detecting diachronic semantic shift
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A framework for the computational linguistic analysis of dehumanization
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Artificial intelligence: A modern approach , 4th edition
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