Improving dependency parsing with semantic classes

  • Authors:
  • Eneko Agirre;Kepa Bengoetxea;Koldo Gojenola;Joakim Nivre

  • Affiliations:
  • University of the Basque Country, UPV/EHU;University of the Basque Country, UPV/EHU;University of the Basque Country, UPV/EHU;Uppsala University

  • Venue:
  • HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents the introduction of WordNet semantic classes in a dependency parser, obtaining improvements on the full Penn Treebank for the first time. We tried different combinations of some basic semantic classes and word sense disambiguation algorithms. Our experiments show that selecting the adequate combination of semantic features on development data is key for success. Given the basic nature of the semantic classes and word sense disambiguation algorithms used, we think there is ample room for future improvements.