The parameters of the random forest classifiers at the problem type, sentence type and sign prediction stages were independently tuned by 5fold cross validation on the training data set choosing the set that achieves the highest cross-validation accuracy.
with n as the number of total available features the problem type prediction classifier was set to have a maximum of p n features and allowed to reach a maximum depth of 15 nodes.