Word Embedding

a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. . . . A word embedding, trained on word co-occurrence in text corpora, represents each word (or common phrase) w as a d-dimensional word vector w~ 2 Rd. It serves as a dictionary of sorts for computer programs that would like to use word meaning. First, words with similar semantic meanings tend to have vectors that are close together. Second, the vector differences between words in embeddings have been shown to represent relationships between words.