# How to find similar document with new vocabulary

I am working on a problem of finding similar documents. I am using a Tf-Idf based vector space model representation of documents and it gives me good results.

However when I encounter a document with new words which is out of the current vector space, it gives an error. For example I have two documents represented as vectors of terms with their weights Document A -> (A,B,C)--> ( 0.15,0.4,0.3) Document B->(C,D,E)--> (0.12,0.3,0.5)

How to compute similarity in these situations and so on

Is there an implementation in python or nltk which takes care of incrementally extending the vector space as new documents are entered.