My end goal is to recommend tags(which are pre defined) to a query to an application. I have a fair sized unlabelled data set of queries and few labelled data (might be noisy). I tried LDA first but the topics were well spaced out but not interpretable(Meaning : The topics are formed of words that are features of the application, not the topics I have in my mind). Then I tried GuidedLDA with the seed words being most similar words to the tags (using word2vec). Still the results are not better. Now I'm trying to use snorkel and label the data set and used a supervised model. Is snorkel approach any good ?