 Word embedding, such as "word2vec", can be thought of as a representational layer in a deep learning architecture that transforms an atomic word into a positional representation of the word relative to other words in the dataset; the position is represented as a point in a vector space.Using word embedding as an RNN input layer allows the network to parse sentences and phrases using an effective compositional vector grammar.A compositional vector grammar can be thought of as probabilistic context free grammar (PCFG) implemented by an RNN.