Also, the employment of SVM classifiers allows the incorporation of rich features for better data representation (Feng and Hirst, 2012).
hILDA's approach also has obvious weakness: the greedy algorithm may lead to poor performance due to local optima, and more importantly, the SVM classifiers are not well-suited for solving structural problems due to the difficulty of taking context into account.