Semantic Classes and Relevant Domains on WSD

  • Rubén Izquierdo
  • Sonia Vázquez
  • Andrés Montoyo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8655)


Language ambiguities are a problem in various fields. For example, in Machine Translation the major cause of errors is ambiguity. Moreover, ambiguous words can be confusing for Information Extraction algorithms. Our purpose in this work is to provide a new approach to solve semantic ambiguities by dealing with the problem of the fine granularity of sense inventories. Our goal is to replace word senses with Semantic Classes that share properties, features and meanings. Also another semantic resources, Relevant Domains, is used to extract extract semantic information and enrich the process. The results obtained are evaluated in the Evaluation Exercises for the Semantic Analysis of Text (SensEval) framework.


Target Word Natural Language Processing Machine Translation Semantic Feature Ambiguous Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rubén Izquierdo
    • 1
  • Sonia Vázquez
    • 2
  • Andrés Montoyo
    • 2
  1. 1.Computational Lexicology and Terminology LabVrije University of AmsterdamThe Netherlands
  2. 2.Department of Software and Computing SystemsUniversity of AlicanteSpain

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