The framework relies on so called word embedding: ∆ D → R P , where P is the dimensionality of the embedding space.
the text sequence X is represented via the respective word embedding for each token: A typical text classification method proceeds in three steps, endto-end, by considering a function decomposition , the text sequence is represented as its word-embedding form V, which is a matrix of P × L. • f 1 : V → z, a compositional function f 1 aggregates word embeddings into a fixed-length vector representation z.