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Consider a continuous word embedding model.
Distributional structure
Zellig S Harris · 1954
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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
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Principal component analysis
Ian Jolliffe · 2002
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Reading tea leaves: How humans interpret topic models
Jonathan Chang, Jordan L Boyd-Graber, Sean Gerrish, Chong Wang, and David M Blei · 2009
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Automatic evaluation of topic coherence
David Newman, Jey Han Lau, Karl Grieser, and Timothy Baldwin · 2010
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Learning effective and interpretable semantic models using non-negative sparse embedding
Brian Murphy, Partha Pratim Talukdar, and Tom Mitchell · 2012
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Evaluating topic coherence using distributional semantics
Nikolaos Aletras and Mark Stevenson · 2013
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality
Jey Han Lau, David Newman, and Timothy Baldwin · 2014
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg · 2014
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Linguistic regularities in sparse and explicit word representations
Omer Levy, Yoav Goldberg, and Israel Ramat-Gan · 2014
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Evaluating three corpus-based semantic similarity systems for Russian
N Arefyev, A Panchenko, A Lukanin, O Lesota, and P Romanov · 2015
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Eigenwords: Spectral word embeddings
Paramveer S. Dhillon, Dean P. Foster, and Lyle H. Ungar · 2015
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Texts in, meaning out: Neural language models in semantic similarity tasks for Russian
A. Kutuzov and I. Andreev · 2015
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CICLing 2015 proceedings, Part I
Andrey Kutuzov and Elizaveta Kuzmenko · 2015
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Online learning of interpretable word embeddings
Hongyin Luo, Zhiyuan Liu, Huan-Bo Luan, and Maosong Sun · 2015
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Russe: The first workshop on russian semantic similarity
A. Panchenko, N. V. Loukachevitch, D. Ustalov, D. Paperno, C. M. Meyer, and N. Konstantinova · 2015
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Using embedding masks for word categorization
Stefan Ruseti, Traian Rebedea, and Stefan Trausan-Matu · 2016
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Correlation-based intrinsic evaluation of word vector representations
Yulia Tsvetkov, Manaal Faruqui, and Chris Dyer · 2016
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A simple but tough-to-beat baseline for sentence embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma · 2017
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Explaining and generalizing skip-gram through exponential family principal component analysis
Ryan Cotterell, Adam Poliak, Benjamin Van Durme, and Jason Eisner · 2017
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Riemannian optimization for skip-gram negative sampling
Alexander Fonarev, Oleksii Hrinchuk, Gleb Gusev, Pavel Serdyukov, and Ivan Oseledets · 2017
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Elucidating conceptual properties from word embeddings
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Intrinsic evaluations of word embeddings: What can we do better?
Anna Gladkova, Aleksandr Drozd, and Computing Center · 2016
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Topic quality metrics based on distributed word representations
Sergey I Nikolenko · 2016
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Word embedding calculus in meaningful ultradense subspaces
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WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models
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A. Panchenko, D. Ustalov, N. Arefyev, D. Paperno, N. Konstantinova, N. Loukachevitch, and C. Biemann · 2017
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Measuring topic coherence through optimal word buckets
Nitin Ramrakhiyani, Sachin Pawar, Swapnil Hingmire, and Girish K Palshikar · 2017
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Offline bilingual word vectors, orthogonal transformations and the inverted softmax
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