I am not native English speaker and often times I use Ozdic to find the correct word choice. It's good overall but its database is quite limited. I think a tool based on Word2Vec should be able to do a great job for finding collocations/cooccurance.

With skip-grams, given a window size of n words around a word w, word2vec predicts contextual words c; i.e. in the notation of probability p(c|w). Conversely, CBOW predicts the current word, given the context in the window, p(w|c).

I am looking for a tool that takes a sentence with a blank spot as input and gives alternative options for the blank spot as an output.

  • $\begingroup$ Is there any reason why it has to include DL? $\endgroup$
    – rabbit
    Sep 9, 2017 at 0:10
  • $\begingroup$ Now that I think of it, not really. Perhaps I was under the influence of DL hype! $\endgroup$
    – Thoran
    Sep 9, 2017 at 20:48

1 Answer 1


You are describing the "Language Modeling" problem in NLP. Language modeling finds the probability distribution over sequences of words - given the words I have seen so far, what are the most likely words that will come next or are missing.

word2vec can find reasonable collocations/cooccurrence for individual words. However, word2vec will do a poor job with sentence completion because it does not model grammatical dependencies.

You can find a word2vec nearest individual word demo here.


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