Word Embeddings vs One-Hot Encoding

Word embeddings require no training data Word embeddings capture semantic similarity in dense vectors, whereas one-hot vectors are sparse and encode no notion of similarity One-hot encoding always produces smaller vectors than word embeddings Word embeddings guarantee that antonyms have opposite vector directions