Since the dependencies between constituents can be an exponential number and representing structures in learning algorithms is rather challenging, automatic feature engineering through kernel methods (Shawe-Taylor and Cristianini, 2004;Moschitti, 2006) can be a promising direction.
we used default parameters both for P T K and SP T K whereas we selected h and D parameters of N SP DK that obtained the best average accuracy using a 5-fold cross validation on the training set.