Translations:Efficient Estimation of Word Representations/28/en

Word2Vec transformed NLP by establishing word embeddings as the standard input representation for neural NLP systems. Before Word2Vec, most NLP systems relied on sparse, high-dimensional representations like one-hot vectors or TF-IDF. Word2Vec demonstrated that dense, low-dimensional vectors could capture rich linguistic structure and transfer meaningfully across tasks.