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            <namePart type="given">Martha</namePart>
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    <abstract>Despite their ubiquity, word embeddings trained with skip-gram negative sampling (SGNS) remain poorly understood. We find that vector positions are not simply determined by semantic similarity, but rather occupy a narrow cone, diametrically opposed to the context vectors. We show that this geometric concentration depends on the ratio of positive to negative examples, and that it is neither theoretically nor empirically inherent in related embedding algorithms.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1308</identifier>
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%0 Conference Proceedings
%T The strange geometry of skip-gram with negative sampling
%A Mimno, David
%A Thompson, Laure
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F mimno-thompson-2017-strange
%X Despite their ubiquity, word embeddings trained with skip-gram negative sampling (SGNS) remain poorly understood. We find that vector positions are not simply determined by semantic similarity, but rather occupy a narrow cone, diametrically opposed to the context vectors. We show that this geometric concentration depends on the ratio of positive to negative examples, and that it is neither theoretically nor empirically inherent in related embedding algorithms.
%R 10.18653/v1/D17-1308
%U https://aclanthology.org/D17-1308
%U https://doi.org/10.18653/v1/D17-1308
%P 2873-2878

Markdown (Informal)

[The strange geometry of skip-gram with negative sampling](https://aclanthology.org/D17-1308) (Mimno & Thompson, EMNLP 2017)

• The strange geometry of skip-gram with negative sampling (Mimno & Thompson, EMNLP 2017)

ACL

• David Mimno and Laure Thompson. 2017. The strange geometry of skip-gram with negative sampling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2873–2878, Copenhagen, Denmark. Association for Computational Linguistics.