Convolution Kernels for Natural Language

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Michael Collins, Nigel Duffy


We describe the application of kernel methods to Natural Language Pro- cessing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discrete structures which require some mechanism to convert them into feature vectors. We describe kernels for various natural language structures, allowing rich, high dimensional rep- resentations of these structures. We show how a kernel over trees can be applied to parsing using the voted perceptron algorithm, and we give experimental results on the ATIS corpus of parse trees.