I am new to NLP and I'm trying to perform embedding for a clustering problem. I have created the word2vec model using Python's gensim library, but I am wondering the following:

The word2vec model embeds the words to vectors of size vector_size. However, in further steps of the clustering approach, I realised I was clustering based on single words instead of the sentences I had in my dataset at the beginning.

Let's say my vocabulary is composed of the two words foo and bar, mapped as follows:

foo: [0.0045, -0.0593, 0.0045]
bar: [-0.943, 0.05311, 0.5839]

If I have a sentence bar foo, how can I embed it? I mean, how can I get the vector of the entire sentence as a whole?

Thanks in advance.


1 Answer 1


The usual approach is to average the vectors of all words in the sentence.

  • $\begingroup$ This was my first thought too. However, I have just realised that there is a Doc2Vec model that basically appends each word's vector. At the end, I would have to average all of the vectors appended by Doc2Vec I guess? $\endgroup$
    – ixperdomo
    Feb 14 at 8:30
  • $\begingroup$ Sorry, I have never used doc2vec, so I can't answer the question in your comment. You may post it as a separate question to get answers to it. $\endgroup$
    – noe
    Feb 14 at 9:47

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.