Consequently, word embeddings have been commonly used in the last few years for lexical similarity tasks and as features in multiple, syntactic and semantic, NLP applications.
keeping embedding vectors for hundreds of thousands of words for repeated use could take its toll both on storing the word vectors on disk and, even more so, on loading them into memory.