How do embedding spaces encode semantic structure?

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

Neural embeddings are trained such that words or sentences with similar meanings have close vector representations. These spaces capture analogies, clustering, and syntactic patterns. However, they are not perfectly linear or interpretable.