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arXiv · 1606.00819

Matrix Factorization using Window Sampling and Negative Sampling for Improved Word Representations

Alexandre Salle·Marco Idiart·Aline Villavicencio
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Abstract

In this paper, we propose LexVec, a new method for generating distributed word representations that uses low-rank, weighted factorization of the Positive Point-wise Mutual Information matrix via stochastic gradient descent, employing a weighting scheme that assigns heavier penalties for errors on frequent co-occurrences while still accounting for negative co-occurrence. Evaluation on word similarity and analogy tasks shows that LexVec matches and often outperforms state-of-the-art methods on many of these tasks.

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Alexandre Salle, Marco Idiart, Aline Villavicencio. 2016-06-02. Matrix Factorization using Window Sampling and Negative Sampling for Improved Word Representations. https://arxiv.org/abs/1606.00819

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