I finished top 25 in kaggle using NLTK and sklearn. word2vec is thrown around like the gospel in NLP, but simple techniques usually do a lot better because: #1

I finished top 25 in kaggle using NLTK and sklearn. word2vec is thrown around like the gospel in NLP, but simple techniques usually do a lot better because: #1 there isnt that much data in most cases and most importantly #2 the corpus differs substantially than the one word2vec was fit on. I am really flabbergasted by how many people start with word2vec and LSTM and come up with really over-fit models and they never even tried the simple things. using ngrams (1 and 2 on words, and 3-5 characters with truncated SVD) gets you really far.