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word2vec Skip-Gram with Negative Sampling is a Weighted Logistic PCA
May 27, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Andrew J. Landgraf, Jeremy Bellay
arXiv ID
1705.09755
Category
cs.CL: Computation & Language
Cross-listed
stat.ML
Citations
21
Venue
arXiv.org
Last Checked
4 months ago
Abstract
We show that the skip-gram formulation of word2vec trained with negative sampling is equivalent to a weighted logistic PCA. This connection allows us to better understand the objective, compare it to other word embedding methods, and extend it to higher dimensional models.
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