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Word embeddings are an essential component in a wide range of natural language processing applications.
Using tf-idf to determine word relevance in document queries
Juan Ramos et al. 2003 · 2003
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The Westbury Lab Wikipedia Corpus
Cyrus Shaoul. 2010 · 2010
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Improving word representations via global context and multiple word prototypes
Eric H Huang, Richard Socher, Christopher D Manning, and Andrew Y Ng. 2012 · 2012
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Better word representations with recursive neural networks for morphology
Thang Luong, Richard Socher, and Christopher Manning. 2013 · 2013
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Propose but verify: Fast mapping meets cross-situational word learning
John C Trueswell, Tamara Nicol Medina, Alon Hafri, and Lila R Gleitman. 2013 · 2013
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Word2Vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method
Yoav Goldberg and Omer Levy. 2014 · 2014
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Dependency-based word embeddings
Omer Levy and Yoav Goldberg. 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Improving distributional similarity with lessons learned from word embeddings
Omer Levy, Yoav Goldberg, and Ido Dagan. 2015 · 2015
Cited alongside, same era.
Cross-lingual word embeddings for low-resource language modeling
Oliver Adams, Adam Makarucha, Graham Neubig, Steven Bird, and Trevor Cohn. 2017 · 2017
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
High-risk learning: acquiring new word vectors from tiny data
Aurélie Herbelot and Marco Baroni. 2017 · 2017
Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. 2017 · 2017
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Multimodal word meaning induction from minimal exposure to natural text
Angeliki Lazaridou, Marco Marelli, and Marco Baroni. 2017 · 2017
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A la carte embedding: Cheap but effective induction of semantic feature vectors
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, and Sanjeev Arora. 2018 · 2018
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Learning semantic representations for novel words: Leveraging both form and context
Timo Schick and Hinrich Schütze. 2018 · 2018
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Memory, show the way: Memory based few shot word representation learning
Jingyuan Sun, Shaonan Wang, and Chengqing Zong. 2018 · 2018
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Cited alongside, same era.
Words are vectors, dependencies are matrices: Learning word embeddings from dependency graphs
Paula Czarnowska, Guy Emerson, and Ann Copestake. 2019 · 2019
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