Update gensim word2vec model

Question

I have a word2vec model in gensim trained over 98892 documents. For any given sentence that is not present in the sentences array (i.e. the set over which I trained the model), I need to update the model with that sentence so that querying it next time gives out some results. I am doing it like this:

new_sentence = ['moscow', 'weather', 'cold'] model.train(new_sentence) 

and its printing this as logs:

2014-03-01 16:46:58,061 : INFO : training model with 1 workers on 98892 vocabulary and 100 features 2014-03-01 16:46:58,211 : INFO : reached the end of input; waiting to finish 1 outstanding jobs 2014-03-01 16:46:58,235 : INFO : training on 10 words took 0.1s, 174 words/s 

Now, when I query with similar new_sentence for most positives (as model.most_similar(positive=new_sentence)) it gives out error:

Traceback (most recent call last):  File "<pyshell#220>", line 1, in <module>  model.most_similar(positive=['moscow', 'weather', 'cold'])  File "/Library/Python/2.7/site-packages/gensim/models/word2vec.py", line 405, in most_similar  raise KeyError("word '%s' not in vocabulary" % word)   KeyError: "word 'cold' not in vocabulary" 

Which indicates that the word 'cold' is not part of the vocabulary over which i trained the thing (am I right)?

So the question is: How to update the model so that it gives out all the possible similarities for the given new sentence?

Answer 1

1. train() expects a sequence of sentences on input, not one sentence.

2. train() only updates weights for existing feature vectors based on existing vocabulary. You cannot add new vocabulary (=new feature vectors) using train().

Answer 2

As of gensim 0.13.3 it's possible to do online training of Word2Vec with gensim.

model.build_vocab(new_sentences, update=True) model.train(new_sentences)