In a nutshell
Word embeddings turn each word into a vector — a list of numbers placing it as a point in a space where geometry encodes meaning. Words used in similar ways sit close together, and consistent relationships become consistent directions, so king − man + woman lands near queen. Methods like word2vec learn this layout from raw text alone, with no labels, by noticing which words tend to appear near each other.