How do vector embeddings demonstrate semantic proximity when representing words as multi-dimensional coordinates?

Vector embeddings represent words as lists of numbers called coordinates, which pinpoint a word's location within a high-dimensional mathematical space. Each dimension in this space represents a latent feature of meaning, derived from how often words appear in similar contexts during the training of a machine learning model. Semantic proximity is demonstrated through the geometric distance between th....