The core principle involves mapping discrete linguistic tokens into a high-dimensional Euclidean space where semantic meaning is encoded via vector proximity and linear superposition properties. This mechanism relies on the formal definition of embeddings within continuous variable spaces, establishing that words sharing similar contexts converge toward specific geometric manifolds in ℝⁿ while maintaining orthogonality for distinct concepts. As a foundational subfield of computational linguistics and representation learning, this theory provides the theoretical bedrock for subsequent operations involving semantic retrieval and pattern recognition without relying on symbolic logic or discrete state transitions.
Explains why words must become numbers, embedding layers as learned lookup tables, word2vec's continuous bag-of-words and skip-gram training, negative sampling, and how similar words end up with simi…