# Outperforming Word2Vec on Analogy Tasks with Random Projections

@article{Demski2014OutperformingWO, title={Outperforming Word2Vec on Analogy Tasks with Random Projections}, author={A. Demski and Volkan Ustun and P. Rosenbloom and C. Kommers}, journal={ArXiv}, year={2014}, volume={abs/1412.6616} }

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… Expand

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