where S i is a possible subtree, # S i (T j ) is the number of times S i is included in T j , and W (S i ) is the weight of S i .
tree kernels are inner products in a subtree feature space where a tree is mapped to vector V (T j  With tree kernels we can take global structures into account, while alleviating overfitting with kernelbased learning methods such as support vector machines (SVMs) (Vapnik, 1995).