Most of the learning algorithms for NLP problems are based on vector space models, which represent data as vectors φ ∈ R n , and try to learn feature weight vectors w ∈ R n such that a linear model y = w • φ is able to discriminate between, say, good and bad hypotheses.
it is hard to know the structure of target feature weights before learning, and it would be impractical to try every possible combination of mode sizes, therefore we choose the criterion of determining the mode sizes as minimization of the total number of parameters, namely we solve the problem: The optimal solution is reached when D is an integer, therefore we choose extra entries of the tensor correspond to no features and are used just for padding.