<p>First, are you sure removing unknown terms is so bad? (Have you tried it?) If there were no examples of those terms in your training data, they may not be that common or important – at least not until you have more training data with many varied examples of their use. And even if they are relevant, until you have many examples up front, you can't know much about their significance. </p>

<p>As you note, you could re-train a Word2Vec model including the new examples, and thus get some vague idea of where the unknown words belong. You could conceivably then re-train any downstream classifier using all the new word-vectors, or project the new-word back into the original space. (This could use a method similar to that described in section 4 of the <em><a href="https://arxiv.org/abs/1309.4168" rel="nofollow noreferrer">Exploiting Similarities among Languages for Machine Translation</a></em> paper, except now your two 'languages' are the models before and after the new word(s) are added.) But if you're only working from a few such word-occurrences, or perhaps in a single new text, everything you'll learn about that word is already a function of the surrounding words already available to your classifier, so the gains might be quite small. (That is, it's only the heftier meaning that comes from many diverse examples elsewhere that's likely to add to understanding of a new text, beyond its existing context.)</p>

<p>Some variants of word2vec, like Facebook's fastText, also learn vectors for fragments of words. Those are combined to make the full word vectors. Then when new out-of-original-vocabulary words are encountered later, they can synthesize word-vectors from the shared fragments. When the new word is morphologically related to a known word, this strategy can do OK. So you may want to take a look at FastText. (It also has a mode where classification-labels are mixed into word-vector training, which can serve to make the word-vectors better for later classification among the same labels.)</p>
Startegies to deal with new terms in test data set
<p>I'm using word2vec model to build a classifier on training data set and wonder what are technics to deal with unseen terms (words) in the test data.</p>

<p>Removing new terms doesn't seem like a best approach.
My current thought is to recalculate word2vec on combined data set (training + test) and replace new terms with nearest word from training data set (or maybe some linear combination of 2-3 nearest). Sounds a bit tricky, but should be doable.</p>

<p>Have you come across similar problem? Any idea/suggestion how to deal with unseen terms?</p>