  We propose a computationally light method for estimating similarities between
text documents, which we call the density similarity (DS) method. The method is
based on a word embedding in a high-dimensional Euclidean space and on kernel
regression, and takes into account semantic relations among words. We find that
the accuracy of this method is virtually the same as that of a state-of-the-art
method, while the gain in speed is very substantial. Additionally, we introduce
generalized versions of the top-k accuracy metric and of the Jaccard metric of
agreement between similarity models.
