Paper: Reverse Engineering of Tree Kernel Feature Spaces

ACL ID D09-1012
Title Reverse Engineering of Tree Kernel Feature Spaces
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

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 kernel- based machine, e.g. Support Vector Ma- chines (SVMs). In particular, our min- ing algorithm selects the most relevant fea- tures based on SVM estimated weights and uses this information to automatically infer an explicit representation of the in- put data. The explicit features (a) improve our knowledge on the target problem do- main and (b) make large-scale learning practical, improving training and test time, while yielding accuracy in line with tradi- tional TK classifiers. Experiments on se- mantic role labeling and question classifi- cation illustrate the above claims.