What exactly does word2vec learn?
Evolving story · 1 updatesTheoretical Breakthrough in Word2Vec LearningTimeline →Researchers from UC Berkeley provide a quantitative theory explaining how word2vec learns word embeddings, reducing the learning process to unweighted least-squares matrix factorization in practical regimes.
What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task. Despite the fact that word2vec is a well-known precursor to modern language models, for many years, researchers lacked a quantitative and predictive theory describing its learning process. In our new paper, we finally provide such a theory. We prove that there are realistic, practical regimes in which the learning problem reduces to unweighted least-squares matrix factorization. We solve the gradient flow dynamics in closed for
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