UBC-ALM: combining k-NN with SVD for WSD

  • Authors:
  • Eneko Agirre;Oier Lopez de Lacalle

  • Affiliations:
  • University of the Basque Country, Donostia, Basque Country;University of the Basque Country, Donostia, Basque Country

  • Venue:
  • SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
  • Year:
  • 2007

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Abstract

This work describes the University of the Basque Country system (UBC-ALM) for lexical sample and all-words WSD subtasks of SemEval-2007 task 17, where it performed in the second and fifth positions respectively. The system is based on a combination of k-Nearest Neighbor classifiers, with each classifier learning from a distinct set of features: local features (syntactic, collocations features), topical features (bag-of-words, domain information) and latent features learned from a reduced space using Singular Value Decomposition.