SVM based learning system for information extraction

  • Authors:

  • Yaoyong Li;Kalina Bontcheva;Hamish Cunningham

  • Affiliations:

  • Department of Computer Science, The University of Sheffield, Sheffield, UK;Department of Computer Science, The University of Sheffield, Sheffield, UK;Department of Computer Science, The University of Sheffield, Sheffield, UK

  • Venue:
  • Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful for small training datasets. In addition, our approach needs fewer SVM classifiers to be trained than other recent SVM-based systems. The paper also compares our approach to several state-of-the-art systems (including rule learning and statistical learning algorithms) on three IE benchmark datasets: CoNLL-2003, CMU seminars, and the software jobs corpus. The experimental results show that our system outperforms a recent SVM-based system on CoNLL-2003, achieves the highest score on eight out of 17 categories on the jobs corpus, and is second best on the remaining nine.