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arXiv · 1412.6616

Outperforming Word2Vec on Analogy Tasks with Random Projections

Abram Demski·Volkan Ustun·Paul Rosenbloom·Cody Kommers
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Abstract

We present a distributed vector representation based on a simplification of the BEAGLE system, designed in the context of the Sigma cognitive architecture. Our method does not require gradient-based training of neural networks, matrix decompositions as with LSA, or convolutions as with BEAGLE. All that is involved is a sum of random vectors and their pointwise products. Despite the simplicity of this technique, it gives state-of-the-art results on analogy problems, in most cases better than Word2Vec. To explain this success, we interpret it as a dimension reduction via random projection.

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Abram Demski, Volkan Ustun, Paul Rosenbloom, Cody Kommers. 2015-02-17. Outperforming Word2Vec on Analogy Tasks with Random Projections. https://arxiv.org/abs/1412.6616

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