Two birds with one stone: learning semantic models for text categorization and word sense disambiguation

  • Authors:

  • Roberto Navigli;Stefano Faralli;Aitor Soroa;Oier de Lacalle;Eneko Agirre

  • Affiliations:

  • Sapienza University of Rome, Rome, Italy;Sapienza University of Rome, Rome, Italy;University of the Basque Country, Donostia, Spain;University of the Basque Country, Donostia, Spain;University of the Basque Country, Donostia, Spain

  • Venue:
  • Proceedings of the 20th ACM international conference on Information and knowledge management
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper we present a novel approach to learning semantic models for multiple domains, which we use to categorize Wikipedia pages and to perform domain Word Sense Disambiguation (WSD). In order to learn a semantic model for each domain we first extract relevant terms from the texts in the domain and then use these terms to initialize a random walk over the WordNet graph. Given an input text, we check the semantic models, choose the appropriate domain for that text and use the best-matching model to perform WSD. Our results show considerable improvements on text categorization and domain WSD tasks.