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        <title>Riemannian Optimization for Skip-Gram Negative Sampling</title>
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        <namePart type="given">Alexander</namePart>
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    <name type="personal">
        <namePart type="given">Pavel</namePart>
        <namePart type="family">Serdyukov</namePart>
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    <name type="personal">
        <namePart type="given">Ivan</namePart>
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            <namePart type="given">Min-Yen</namePart>
            <namePart type="family">Kan</namePart>
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    <abstract>Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in “word2vec” software, is usually optimized by stochastic gradient descent. However, the optimization of SGNS objective can be viewed as a problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.</abstract>
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    <identifier type="doi">10.18653/v1/P17-1185</identifier>
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%0 Conference Proceedings
%T Riemannian Optimization for Skip-Gram Negative Sampling
%A Fonarev, Alexander
%A Grinchuk, Oleksii
%A Gusev, Gleb
%A Serdyukov, Pavel
%A Oseledets, Ivan
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F fonarev-etal-2017-riemannian
%X Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in “word2vec” software, is usually optimized by stochastic gradient descent. However, the optimization of SGNS objective can be viewed as a problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.
%R 10.18653/v1/P17-1185
%U https://aclanthology.org/P17-1185
%U https://doi.org/10.18653/v1/P17-1185
%P 2028-2036

Markdown (Informal)

[Riemannian Optimization for Skip-Gram Negative Sampling](https://aclanthology.org/P17-1185) (Fonarev et al., ACL 2017)

• Riemannian Optimization for Skip-Gram Negative Sampling (Fonarev et al., ACL 2017)

ACL

• Alexander Fonarev, Oleksii Grinchuk, Gleb Gusev, Pavel Serdyukov, and Ivan Oseledets. 2017. Riemannian Optimization for Skip-Gram Negative Sampling. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2028–2036, Vancouver, Canada. Association for Computational Linguistics.