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Reverse Engineering of Tree Kernel Feature Spaces
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BibTeX
@MISC{Pighin_reverseengineering,
author = {Daniele Pighin and Alessandro Moschitti},
title = {Reverse Engineering of Tree Kernel Feature Spaces},
year = {}
}
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Abstract
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. 1
Keyphrases
tree kernel feature space reverse engineering target problem domain tree kernel test time input data traditional tk classifier mining algorithm support vector machine tree fragment explicit feature explicit representation semantic role labeling question classification relevant feature important feature
