Reverse engineering of tree kernel feature spaces

Daniele Pighin, Alessandro Moschitti

Research output: Contribution to conferencePaper

16 Citations (Scopus)

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.

Original languageEnglish
Pages111-120
Number of pages10
Publication statusPublished - 1 Dec 2009
Event2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 - Singapore, Singapore
Duration: 6 Aug 20097 Aug 2009

Other

Other2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009
CountrySingapore
CitySingapore
Period6/8/097/8/09

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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    Pighin, D., & Moschitti, A. (2009). Reverse engineering of tree kernel feature spaces. 111-120. Paper presented at 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009, Singapore, Singapore.