id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_hmfm2qy66veqtbcqmo7nvj6ttu	Sanjeev Arora	A Latent Variable Model Approach to PMI-based Word Embeddings	2016	16	.pdf	application/pdf	9773	1139	70	A Latent Variable Model Approach to PMI-based Word Embeddings Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are found to be closely approximated by a low rank matrix: there exist word vectors in say 300 dimensions, analogies like "man:woman::king:??," queen happens to be the word whose vector vqueen is the most that the set of all word vectors (which are latent variables of the generative model) are spatially isotropic, out the hidden random variables and compute a simple closed form expression that approximately connects the model parameters to the observable joint vectors need to have varying lengths, to fit the empirical finding that word probabilities satisfy a power If the word vectors satisfy the Bayesian prior described in the model details, then Our model assumed the set of all word vectors theoretical explanation of RELATIONS=LINES assumes that the matrix of word vectors behaves like	./cache/work_hmfm2qy66veqtbcqmo7nvj6ttu.pdf	./txt/work_hmfm2qy66veqtbcqmo7nvj6ttu.txt