Stanford University School of Engineering
Word Embeddings in Natural Language Processing
Dense vector representations of words learned so that distributional similarity is reflected as geometric proximity, including methods such as Word2Vec and GloVe and contextual variants. Covers how such embeddings encode lexical meaning and are pooled into features for downstream text tasks.