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Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent.
Convex functions and optimization methods on Riemannian manifolds
Constantin Udriste. 1994 · 1994
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Placing search in context: The concept revisited
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Dynamical low-rank approximation
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A study on similarity and relatedness using distributional and wordnet-based approaches
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Distributed representations of words and phrases and their compositionality
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word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method
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word2vec parameter learning explained
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Towards a better understanding of predict and count models
S Sathiya Keerthi, Tobias Schnabel, and Rajiv Khanna. 2015 · 2015
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How to generate a good word embedding?
Siwei Lai, Kang Liu, Shi He, and Jun Zhao. 2015 · 2015
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Improving distributional similarity with lessons learned from word embeddings
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On the degrees of freedom of reduced-rank estimators in multivariate regression
A Mukherjee, K Chen, N Wang, and J Zhu. 2015 · 2015
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Evaluation methods for unsupervised word embeddings
Tobias Schnabel, Igor Labutov, David Mimno, and Thorsten Joachims. 2015 · 2015
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Guarantees of riemannian optimization for low rank matrix recovery
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