lda2vec: Tools for interpreting natural language • /r/MachineLearning

It builds off of paragraph vectors. But paragraph vectors aren't interpretable (or at least as interpretable) as LDA-like vectors; I can't hand my CEO a 512-dimensional paragraph vector to show her what's been trending. But I can hand her an LDA vector, because it's a sparse mixture of interpretable vectors. Because that vector is essentially'on' in three or four categories, those categories sum to 100%, and you don't have to deal with negative coefficients. The whole point here is to gear the internal representations so they're more amenable to humans -- it'd be cute if it scored better, but life isn't a Kaggle contest.