that's a nice link but the excerpt you quote is kind of misleading; the word vectors in this case are not one-hot encodings. They are learned, continuous representations. But one-hot representations are also a kind of word vector.

word vectors are vector representations of each word in the vocabulary. Here they are learned by a neural net. the length of the vector is the # of features. Just for intuition, one feature of a word the NN could learn is the gender of a word, and so on.