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This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis.
From frequency to meaning: Vector space models of semantics
Peter D. Turney and Patrick Pantel, 2010 · 1901
Earlier work this paper cites.
Canonical correlation analysis (cca)
Harold Hotelling, 1935 · 1935
Earlier work this paper cites.
Distributional structure
Zellig S. Harris, 1954 · 1954
Earlier work this paper cites.
A vector space model for automatic indexing
G. Salton, A. Wong, and C. S. Yang, November 1975 · 1975
Earlier work this paper cites.
A maximum likelihood approach to continuous speech recognition
L. R. Bahl, F. Jelinek, and R. L. Mercer, March 1983 · 1983
Earlier work this paper cites.
Indexing by latent semantic analysis
Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman, 1990 · 1990
Earlier work this paper cites.
Natural language processing with modular pdp networks and distributed lexicon
Risto Miikkulainen and Michael G. Dyer, 7 1991 · 1991
Earlier work this paper cites.
Class-based n-gram models of natural language
Peter F. Brown, Peter V. deSouza, Robert L. Mercer, Vincent J. Della Pietra, and Jenifer C. Lai, 1992 · 1992
Earlier work this paper cites.
Producing high-dimensional semantic spaces from lexical co-occurrence
Kevin Lund and Curt Burgess, 1996 · 1996
Earlier work this paper cites.
Classes for fast maximum entropy training
Joshua Goodman, 2001 · 2001
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E. Hinton, August 2002 · 2002
Cited alongside, same era.
Quick training of probabilistic neural nets by importance sampling
Yoshua Bengio and Jean-Sébastien Senécal, 2003 · 2003
Cited alongside, same era.
A neural probabilistic language model
Yoshua Bengio, Jean Ducharme, Pascal Vincent, and Christian Janvin, March 2003 · 2003
Cited alongside, same era.
An improved model of semantic similarity based on lexical co-occurence
Douglas L. T. Rohde, Laura M. Gonnerman, and David C. Plaut, 2006 · 2006
Cited alongside, same era.
Extracting semantic representations from word co-occurrence statistics: A computational study
John A. Bullinaria and Joseph P. Levy, 2007 · 2007
Cited alongside, same era.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Word emdeddings through hellinger PCA
Rémi Lebret and Ronan Collobert, 2013 · 2013
Later among the works it cites.
June 2014
Marco Baroni, Georgiana Dinu, and Germán Kruszewski · 2014
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Distributed representations of sentences and documents
Quoc V. Le and Tomas Mikolov, 2014 · 2014
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October 2014
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems
Martín Abadi, Ashish Agarwal, Paul Barham, et al., 2015 · 2015
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Michael Gutmann and Aapo Hyvärinen, 2010 · 2010
Cited alongside, same era.
July 2012
William Blacoe and Mirella Lapata · 2012
Cited alongside, same era.
Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and svd
John A. Bullinaria and Joseph P. Levy, 2012 · 2012
Cited alongside, same era.
Re-embedding words
Igor Labutov and Hod Lipson, 2013 · 2013
Cited alongside, same era.
Log-linear models
Michael Collins
Cited in the paper.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013b
Cited in the paper.
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, and Sanja Fidler, 2015 · 2015
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A generative word embedding model and its low rank positive semidefinite solution
Shaohua Li, Jun Zhu, and Chunyan Miao, 2015 · 2015
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov, 2016 · 2016
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov, 2016 · 2016
Later among the works it cites.