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Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods.
A synopsis of linguistic theory
John Rupert Firth · 1957
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The pricing of options and corporate liabilities
Fischer Black and Myron Scholes · 1973
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Parallel Distributed Processing: Explorations in the Microstructure of Cognition
David E. Rumelhart, Geoffrey E. Hinton, and James L. McClelland, editors · 1986
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Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1988
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Word association norms, mutual information, and lexicography
Kenneth Ward Church and Patrick Hanks · 1990
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Indexing by latent semantic analysis
Scott C. Deerwester, Susan T Dumais, Thomas K. Landauer, George W. Furnas, and Richard A. Harshman · 1990
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Latent semantic indexing: A probabilistic analysis
Christos H. Papadimitriou, Hisao Tamaki, Prabhakar Raghavan, and Santosh Vempala · 1998
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Probabilistic latent semantic analysis
Thomas Hofmann · 1999
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Neural probabilistic language models
Yoshua Bengio, Holger Schwenk, Jean-Sébastien Senécal, Fréderic Morin, and Jean-Luc Gauvain · 2006
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Dynamic topic models
David M. Blei and John D. Lafferty · 2006
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An improved model of semantic similarity based on lexical co-occurence
Douglas L. T. Rohde, Laura M. Gonnerman, and David C. Plaut · 2006
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Euclidean embedding of co-occurrence data
Amir Globerson, Gal Chechik, Fernando Pereira, and Naftali Tishby · 2007
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Three new graphical models for statistical language modelling
Andriy Mnih and Geoffrey Hinton · 2007
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Sphere embedding: An application to part-of-speech induction
Yariv Maron, Michael Lamar, and Elie Bienenstock · 2010
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From frequency to meaning: Vector space models of semantics
Peter D. Turney and Patrick Pantel · 2010
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Spectral learning of latent-variable PCFGs
Shay B. Cohen, Karl Stratos, Michael Collins, Dean P. Foster, and Lyle Ungar · 2012
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A spectral algorithm for learning hidden markov models
Daniel Hsu, Sham M. Kakade, and Tong Zhang · 2012
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When and why are log-linear models self-normalizing?
Jacob Andreas and Dan Klein · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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A latent variable model approach to PMI-based word embeddings
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski · 2015
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A linear dynamical system model for text
David Belanger and Sham M. Kakade · 2015
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Probabilistic topic models
David M. Blei · 2012
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston
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Linguistic regularities in sparse and explicit word representations
Omer Levy and Yoav Goldberg
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Linear algebraic structure of word senses, with applications to polysemy
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski · 2016
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Word embeddings as metric recovery in semantic spaces
Tatsunori B. Hashimoto, David Alvarez-Melis, and Tommi S. Jaakkola · 2016
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