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Efficient Kernel-based Learning for Trees

Kernel methods are effective approaches to the modeling of structured objects in learning algorithms. Their major drawback is the typically high computational complexity of kernel functions. This prevents the application of computational demanding algorithms, e.g. support vector machines, on large datasets. Consequently, on-line learning approaches are required. Moreover, to facilitate the application of kernel methods on structured data, additional efficiency optimization should be carried out. In this paper, we propose direct acyclic graphs to reduce the computational burden and storage requirements by representing common structures and feature vectors. We show the benefit of our approach for the perceptron algorithm using tree and polynomial kernels. The experiments on a quite extensive dataset of about one million of instances show that our model makes the use of kernels for trees practical. From the accuracy point of view, the possibility of using large amount of data has allowed us to reach the state-of-the-art on the automatic detection of semantic role labeling as defined in the conference on natural language learning shared task