The strange geometry of skip-gram with negative sampling

机译：负采样的跳格的奇怪几何

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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.

机译：尽管它们无处不在，但通过跳格负采样（SGNS）训练的词嵌入仍然知之甚少。我们发现向量位置不仅仅由语义相似性确定，而是占据一个与上下文向量截然相反的狭窄圆锥体。我们证明了这种几何集中度取决于正例与负例的比率，并且它在相关的嵌入算法中既不是理论上也不是经验上固有的。

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《Conference on empirical methods in natural language processing》|2017年|2863-2868|共6页
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David Mimno; Laure Thompson;
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The strange geometry of skip-gram with negative sampling

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