I'm just curious are there some alternative techniques to word 2 vector representation? So words/phrases/sentences are not represented as vectors but have a different form. Thanks.

  • 1
    $\begingroup$ Welcome to DataScienceSE. Yes, of course: this is the traditional bag of word representation. $\endgroup$
    – Erwan
    Nov 5, 2022 at 20:41

2 Answers 2


In your question you talk about vector embeddings or "word 2 vector representation" (word2vec was the first software to train word embeddings). It's important to understand that not all vectors are embeddings:

  • Embeddings are short vectors made of real numbers, they were invented around 2010. There are many different types of embeddings, i.e. methods to train the embeddings from a corpus: word2vec, Glove, Elmo, Fasttext, Bert...
  • Before this, people were also using vectors representing a "bag of words": one-hot-encoding for a single word, frequency count or TFIDF for a sentence/document. These vectors are long and sparse, i.e. they usually contain a lot of zeros.

These are the most common word representation methods, but there are potentially other alternatives. For example, in Wordnet the words are nodes in a graph and relations between words are represented as edges.

  • $\begingroup$ Thank you so much. I've finally got some clarity about it. $\endgroup$
    – Dron4K
    Nov 22, 2022 at 18:07

I researched this question and looks like I realized that there are many word representations.

  1. Dictionary lookup

  2. One-hot encoding

  3. Distributional representation

    a. Frequency counts

    b. Word2Vec(Skip-Gram and CBOW)

    c. GloVe

  4. Elmo

  5. Subword

@Erwan provided more correct answer above

  • $\begingroup$ You are confusing things: 'Dictionary lookup' is not a word representation; word2vec, Glove, Elmo and subwords are different word embeddings representations. The one which is correct is in your list is One-hot encoding, i.e. the good old bag of words. $\endgroup$
    – Erwan
    Nov 21, 2022 at 14:38
  • $\begingroup$ @Erwan: I wanted to know how else words can be represented other than as vectors(if at all). You prompted to me that it is bag of words. Then I read about bag of words and figured out that it is still vector representation(e.g. machinelearningmastery.com/gentle-introduction-bag-words-model) So, I thought my question is incorrect. Everything is always a vector. It might be correctly to think about embedding representations. I read this article towardsdatascience.com/… and answered. Sorry for confusing $\endgroup$
    – Dron4K
    Nov 21, 2022 at 19:36
  • $\begingroup$ ok, I tried to explain things better in another answer. $\endgroup$
    – Erwan
    Nov 21, 2022 at 23:58

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

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