Let’s take a look at what Wikipedia has to say about word embeddings — Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers. In other words — word embeddings are vectorized, fixed-length, distributed, dense representations of words that interpret a word’s textual meaning by mapping it to a vector of real values.
