Word embedding is a very popular way of representing text data in problems that are solved by deep learning algorithms. Word embedding provides a dense representation of a word filled with floating numbers. The vector dimension varies according to the vocabulary size. It is common to use a word embedding of dimension size 50, 100, 256, 300, and sometimes 1,000. The dimension size is a hyper-parameter that we need to play with during the training phase.
If we are trying to represent a vocabulary of size 20,000 in one-hot representation then we will end up with 20,000 x 20,000 numbers, most of which will be zero. The same vocabulary can be represented in word embedding as 20,000 x dimension size, where the dimension size could ...
