k = 10, the VLAWE document representation can grow up to thousands of features, as the number of features is k • d, where d = 300 is the dimensionality of commonly used word embeddings.
our experiments on five benchmark data sets prove that our approach yields competitive results with respect to the state-of-the-art methods.