Machine Learning, Deep Learning, Word Embeddings, Natural Language, Artificial Intelligence.
decomposed into components in an infinite number of ways — much like the constant tensor (or scalar) 16 can be decomposed into 14+2, 16+0, SQRT(256), 8 * 2, etc.). Thus, this is not just a flaw of limitations of word embeddings, but of all vector semantics, and is related to the fact that neural