Vector representation are useful for many natural language processing tasks. In word embedding like word2vec, GLoVE, fasttext for each word the algorithm learns an associated vector in dimension n (x1, x2, .., xn). A word maybe close to another in one dimension (or a subspace) but far away in another one. Moreover good representation allows meaningful vector space arithmetic: Queen - Women == King word representations are typically trained on very large unlabeled data, but once the algorithm learns the features you can use them for your small dataset. EDIT: Add more explanations.