Paper: Efficient Linearization of Tree Kernel Functions

ACL ID W09-1106
Title Efficient Linearization of Tree Kernel Functions
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2009

The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two ma- jor drawbacks: first, the implicit representa- tion of feature spaces does not allow us to un- derstand which features actually triggered the generalization; second, the resulting compu- tational burden may in some cases render un- feasible to use large data sets for training. We propose an approach based on feature space reverse engineering to tackle both problems. Our experiments with Tree Kernels on a Se- mantic Role Labeling data set show that the proposed approach can drastically reduce the computational footprint while yielding almost unaffected accuracy.