Document-topic distribution about kd-topic-models HOT 2 OPEN

Thanks for your interest!

To your first question, that function will get document-topic distributions, but it's just a single sample. For a later paper, we modified the function to sample multiple times and take the mean (if I recall correctly, there's no analytical mean for a logistic-normal). You can see the modified code in this branch. In fact, if my commit history is to be trusted, you can view the exact changes here.

Labels (as well as covariates) are optional and all reported results are unsupervised.

Not that you asked, but you should also note that we realized the NPMI implementation in this repo (ported from the original Scholar paper) is nonstandard, and I believe we calculate it during training. You should prefer implementations from Gensim, OCTIS, Palmetto, or us. Of course, the best bet is to forgo automated metrics altogether 😉

from kd-topic-models.

Another thing you didn't ask: we've found that mallet works surprisingly well with Tweets, in case you haven't tried it already and are looking for a good baseline.

from kd-topic-models.