Medium Article: A Dummy's Guide to Word2Vec
Word2Vec creates a representation of each word present in our vocabulary into a vector. Words used in similar contexts or having semantic relationships are captured effectively through their closeness in the vector space- effectively speaking similar words will have similar word vectors! History. Word2vec was created, patented, and published in 2013 by a team of researchers led by Tomas Mikolov at Google.
Hypothetical features to understand word embeddings
We can easily train word2vec word embeddings using Gensim, which is, “is a free open-source Python library for representing documents as semantic vectors, as efficiently (computer-wise) and painlessly (human-wise) as possible.”
In the above notebook, I've demonstrated an implementation of word2vec using the Gensim library.