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Word Sense vs. Word Domain Disambiguation: A Maximum Entropy Approach

  • Armando Suárez
  • Manuel Palomar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2448)

Abstract

In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. The system were evaluated both using WordNet’s senses and domains as the sets of classes of each word. Domain labels are obtained from the enrichment ofWordNet with subject field codes which produces a polysemy reduction. Several types of features has been analyzed for a few words selected from the DSO corpus. Using the domain enrichment of WordNet, a 7% of accuracy improvement is achieved.

Keywords

Maximum Entropy Machine Translation Ambiguous Word Word Sense Statistical Machine Translation 
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-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Armando Suárez
    • 1
  • Manuel Palomar
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

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