# How word2vec can handle unseen / new words to bypass this for new classifications?

In simple terms, if my classification is based on word2vec as features, what I am supposed to do, if a new word comes, which does not have a word2vec?

I am trying to used word2vec or word vectors for classification based on entity.

For example:

I have to classify the following words in a sentence as:

"Google gives information about Nigeria"


Here, I would like to classify Nigeria as location.

Suppose I have good word2vec vectors for each of the words, based on some readings I came to know that, recurrent neural networks can be used for this. So, word2vec will capture most locations with a kind of similar word vectors.

But my questions are:

a) Suppose a new location is there. lets say, Russia . So, do I need to assign a new word vector for this location ?

b) If my input for training does not have grammatical sense. For example,

" Google information Nigeria " . Everything else Nigeria is associated with a non-location label. Will this condition work for find new location in non-grammatical sentences.