For +BOW style models, we take the matrix that compresses the text's word frequency vector, then score each word by computing the l 1 norm of the column that multiplies it, with the intuition that important words are dotted with big vectors in order to be a large component of e. Motivation.
both elicit lexicons by considering learned weights or attentional scores.