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Abstract:
We describe the application of kernel methods to Natural
Language Processing (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 representations 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.
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