In our reranking framework, we also include traditional feature vectors in addition to the trees.
each hypothesis h is represented as a tuple T, v composed of a tree T and a feature vector v. We then define a structural kernel (i.e., similarity) between two hypotheses h and h as follows: where DISCTK maps the DTs T and T to JRN or SRN and then applies STK, STK b or PTK defined in Sections 2.2 and 3, and F V is a standard kernel, e.g., linear, polynomial, gaussian, etc., over feature vectors (see next section).