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In this paper, we propose LexVec, a new method for generating distributed word representations that uses low-rank, weighted factorization of the Positive Point-wise Mutual Information matrix via stochastic gradient descent, employing a weighting scheme that assigns heavier penalties for errors on frequent co-occurrences while still accounting for negative co-occurrence.
The approximation of one matrix by another of lower rank
C. Eckert and G. Young. 1936 · 1936
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Contextual correlates of synonymy
Herbert Rubenstein and John B. Goodenough. 1965 · 1965
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Word association norms, mutual information, and lexicography
Kenneth W. Church and Patrick Hanks. 1990 · 1990
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Contextual correlates of semantic similarity
George A. Miller and Walter G. Charles. 1991 · 1991
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Automatic retrieval and clustering of similar words
Dekang Lin. 1998 · 1998
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Experiments with lsa scoring: Optimal rank and basis
John Caron. 2001 · 2001
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Placing search in context: The concept revisited
Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001 · 2001
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Extracting semantic representations from word co-occurrence statistics: A computational study
John A. Bullinaria and Joseph P. Levy. 2007 · 2007
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Word representations: a simple and general method for semi-supervised learning
Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010 · 2010
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Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
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A word at a time: computing word relatedness using temporal semantic analysis
Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich, and Shaul Markovitch. 2011 · 2011
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Elia Bruni, Gemma Boleda, Marco Baroni, and Nam-Khanh Tran. 2012 · 2012
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Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and svd
John A Bullinaria and Joseph P Levy. 2012 · 2012
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Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors
Marco Baroni, Georgiana Dinu, and Germán Kruszewski. 2014 · 2014
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg. 2014 · 2014
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Linguistic regularities in sparse and explicit word representations
Omer Levy, Yoav Goldberg, and Israel Ramat-Gan. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Simlex-999: Evaluating semantic models with (genuine) similarity estimation
Felix Hill, Roi Reichart, and Anna Korhonen. 2015 · 2015
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Improving distributional similarity with lessons learned from word embeddings
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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
Minh-Thang Luong, Richard Socher, and Christopher D. Manning. 2013 · 2013
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Parsing with compositional vector grammars
Richard Socher, John Bauer, Christopher D Manning, and Andrew Y Ng. 2013 · 2013
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a
Cited in the paper.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013b
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Linguistic regularities in continuous space word representations
Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013c
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Omer Levy, Yoav Goldberg, and Ido Dagan. 2015 · 2015
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Factorization of latent variables in distributional semantic models
Arvid Österlund, David Ödling, and Magnus Sahlgren. 2015 · 2015
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