How do embeddings encode semantic geometry in vector spaces?

Embeddings map semantic similarity into geometric proximity in high-dimensional space.

Embedding spaces encode meaning such that similar concepts cluster together. Linear relationships often emerge (e.g., analogies). However, the geometry is not perfectly Euclidean and depends on training objective and corpus distribution.