In this section, we'll learn about Word2Vec, a modern and popular technique for working with text. Usually, Word2Vec performs better than simple bag of words models. A bag of words model only counts how many times each word appears in each document. Given two such bag of words vectors, we can compare documents to see how similar they are. This is the same as comparing the words used in the documents. In other words, if the two documents have many similar words that appear a similar number of times, they will be considered similar.
But bag of words models have no information about how similar the words are. So, if two documents do not use exactly the same words but do use synonyms, such as please and plz, they're not regarded as similar for the bag of words model. Word2Vec can figure out that some words are similar to each other and we can exploit that fact to get better performance when doing machine learning with text.
In Word2Vec, each word itself is a vector, with perhaps...