Efficient linearization of tree kernel functions

Daniele Pighin, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two major drawbacks: first, the implicit representation of feature spaces does not allow us to understand which features actually triggered the generalization; second, the resulting computational burden may in some cases render unfeasible 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 Semantic Role Labeling data set show that the proposed approach can drastically reduce the computational footprint while yielding almost unaffected accuracy.

Original languageEnglish
Title of host publicationCoNLL 2009 - Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Pages30-38
Number of pages9
Publication statusPublished - 1 Dec 2009
Event13th Conference on Computational Natural Language Learning, CoNLL 2009 - Boulder, CO, United States
Duration: 4 Jun 20095 Jun 2009

Publication series

NameCoNLL 2009 - Proceedings of the Thirteenth Conference on Computational Natural Language Learning

Other

Other13th Conference on Computational Natural Language Learning, CoNLL 2009
CountryUnited States
CityBoulder, CO
Period4/6/095/6/09

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

Cite this

Pighin, D., & Moschitti, A. (2009). Efficient linearization of tree kernel functions. In CoNLL 2009 - Proceedings of the Thirteenth Conference on Computational Natural Language Learning (pp. 30-38). (CoNLL 2009 - Proceedings of the Thirteenth Conference on Computational Natural Language Learning).