Each column vector iñ W ∈ R 2d(c+1)×|W| corresponds to a word.
we assign a logistic regression model for each word, and we can train the embeddings using the one-versus-rest approach to make p(w in i |f ) larger than p(w ′ |f ) for w ′ ̸ = w in i .