Learning noun-modifier semantic relations with corpus-based and WordNet-based features

  • Authors:

  • Vivi Nastase;Jelber Sayyad-Shirabad;Marina Sokolova;Stan Szpakowicz

  • Affiliations:

  • School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;Département d'informatique et de recherche opérationnelle, Université de Montréal, Montréal, Québec, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada and Institute of Computer Science, Polish Academy of Sciences, Warszawa, Poland

  • Venue:
  • AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
  • Year:
  • 2006

Quantified Score

Hi-index 0.01

Visualization

Abstract

We study the performance of two representations of word meaning in learning noun-modifier semantic relations. One representation is based on lexical resources, in particular WordNet, the other - on a corpus. We experimented with decision trees, instance-based learning and Support Vector Machines. All these methods work well in this learning task. We report high precision, recall and F-score, and small variation in performance across several 10-fold cross-validation runs. The corpus-based method has the advantage of working with data without word-sense annotations and performs well over the baseline. The WordNet-based method, requiring word-sense annotated data, has higher precision.