Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using large-scale unlabelled text analysis. However, these representations typically consist of dense vectors that require a great deal of storage and cause the internal structure of the vector space to be opaque. A more 'idealized' representation of a vocabulary would be both compact and readily interpretable. With this goal, this paper first shows that Lloyd's algorithm can compress the standard dense vector representation by a factor of 10 without much loss in performance. Then, using that compressed size as a 'storage budget', we describe a new GPU-friendly factorization procedure to obtain a representation which gains interpretability as a side-effect of being sparse and non-negative in each encoding dimension. Word similarity and word-analogy tests are used to demonstrate the effectiveness of the compressed representations obtained.
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