Reverse Engineering of Tree Kernel Feature Spaces

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Reverse Engineering of Tree Kernel Feature Spaces
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. Support Vector Machines (SVMs). In particular, our mining algorithm selects the most relevant features based on SVM estimated weights and uses this information to automatically infer an explicit representation of the input data. The explicit features (a) improve our knowledge on the target problem domain and (b) make large-scale learning practical, improving training and test time, while yielding accuracy in line with traditional TK classifiers. Experiments on semantic role labeling and question classification illustrate the above claims.
Daniele Pighin, Alessandro Moschitti
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Authors Daniele Pighin, Alessandro Moschitti
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