An SVM with a polynomial kernel takes into account not only individual input features but also their interactions (Manning et al., 2008, chapter 15).
our classifier can capture not just properties of individual dimensions of the antecedent and consequent pairs, but also properties of their combinations (e.g., the product of the first dimensions of the antecedent and the consequent).