At a preprocessing step, we binarize document vectors and remove low frequent words (both in terms  of p(w) and q(w)).
our estimate of |G\R| is twice the number of words with ρ(w) < 1, and then the number of topical words can be estimated as Instead of sharply thresholding topical and nontopical words, we can have them all, weighted with a probability of being topical.