Distributional measures of lexical similarity and kernel methods for classification are well-known tools in Natural Language Processing. We bring these two methods together by introducing distributional kernels that compare co-occurrence probability distributions. We demonstrate the effectiveness of these kernels by presenting state-of-the-art results on datasets for three semantic classification: compound noun interpretation, identification of semantic relations between nominals and semantic classification of verbs. Finally, we consider explanations for the impressive performance of distributional kernels and sketch some promising generalisations.
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