  Structural kernels are a flexible learning paradigm that has been widely used
in Natural Language Processing. However, the problem of model selection in
kernel-based methods is usually overlooked. Previous approaches mostly rely on
setting default values for kernel hyperparameters or using grid search, which
is slow and coarse-grained. In contrast, Bayesian methods allow efficient model
selection by maximizing the evidence on the training data through
gradient-based methods. In this paper we show how to perform this in the
context of structural kernels by using Gaussian Processes. Experimental results
on tree kernels show that this procedure results in better prediction
performance compared to hyperparameter optimization via grid search. The
framework proposed in this paper can be adapted to other structures besides
trees, e.g., strings and graphs, thereby extending the utility of kernel-based
methods.
