Towards Understanding Linear Word Analogies
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A surprising property of word vectors is that vector algebra can often be used to solve word analogies. However, it is unclear why -- and when -- linear operators correspond to non-linear embedding models such as skip-gram with negative sampling (SGNS). We provide a rigorous explanation of this phenomenon without making the strong assumptions that past work has made about the vector space and word distribution. Our theory has several implications. Past work has often conjectured that linear structures exist in vector spaces because relations can be represented as ratios; we prove that this holds for SGNS. We provide novel theoretical justification for the addition of SGNS word vectors by showing that it automatically down-weights the more frequent word, as weighting schemes do ad hoc. Lastly, we offer an information theoretic interpretation of Euclidean distance in vector spaces, providing rigorous justification for its use in capturing word dissimilarity.
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