How word2vec can be used to identify unseen words and relate them to already trained data
Every algorithm that deals with text data has a vocabulary. In the case of word2vec, the vocabulary is comprised of all words in the input corpus, or at least those above the minimum-frequency threshold.

Algorithms tend to ignore words that are outside their vocabulary. However there are ways to reframe your problem such that there are essentially no Out-Of-Vocabulary words.

Remember that words are simply "tokens" in word2vec. They could be ngrams or they could be letters. One way to define your vocabulary is to say that every word that occurs at least X times is in your vocabulary. Then the most common "syllables" (ngrams of letters) are added to your vocabulary. Then you add individual letters to your vocabulary.

In this way you can define any word as either 


A word in your vocabulary
A set of syllables in your vocabulary
A combined set of letters and syllables in your vocabulary