The results show that: (a) on large data sets, our approach can improve training and test time while yielding almost unaffected classification accuracy, and (b) our framework can effectively exploit the ability of TKs and SVMs to, respectively, generate and recognize relevant structured features.
we (i) study in more detail the relevant fragments identfied for the boundary classification task of SRL, (ii) closely observe the most relevant fragments for each QC class and (iii) look at the diverse syntactic patterns characterizing each ques-tion category.