If you're new to word embeddings, you might be feeling a little lost right now. Hang in there, it will become clearer in just a moment. Let's try a concrete example.
Using word2vec, a popular word-embedding model, we can start with the word cat and find it's 384 element vector, as shown in the following output code:
array([ 5.81600726e-01, 3.07168198e+00, 3.73339128e+00, 2.83814788e-01, 2.79787600e-01, 2.29124355e+00, -2.14855480e+00, -1.22236431e+00, 2.20581269e+00, 1.81546474e+00, 2.06929898e+00, -2.71712840e-01,...
I've cut the output short, but you get the idea. Every word in this model is converted into a 384-element vector. These vectors can be compared to evaluate the semantic similarity of words in a dataset.
Now that ...
