Spanish All-Words Semantic Class Disambiguation Using Cast3LB Corpus

  • Rubén Izquierdo-Beviá
  • Lorenza Moreno-Monteagudo
  • Borja Navarro
  • Armando Suárez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


In this paper, an approach to semantic disambiguation based on machine learning and semantic classes for Spanish is presented. A critical issue in a corpus-based approach for Word Sense Disambiguation (WSD) is the lack of wide-coverage resources to automatically learn the linguistic information. In particular, all-words sense annotated corpora such as SemCor do not have enough examples for many senses when used in a machine learning method. Using semantic classes instead of senses allows to collect a larger number of examples for each class while polysemy is reduced, improving the accuracy of semantic disambiguation. Cast3LB, a SemCor-like corpus, manually annotated with Spanish WordNet 1.5 senses, has been used in this paper to perform semantic disambiguation based on several sets of classes: lexicographer files of WordNet, WordNet Domains, and SUMO ontology.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rubén Izquierdo-Beviá
    • 1
  • Lorenza Moreno-Monteagudo
    • 1
  • Borja Navarro
    • 1
  • Armando Suárez
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteSpain

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