Compared with conventional neural network language models which usually set up a multi-layer neural network, Word2Vec merely leverages a threelayer neural network to learn word embeddings, resulting in greatly decreased number of parameters and largely increased scalability.
similar to most of existing word embedding models, Word2Vec also assumes one embedding for one word.