A Supervised Korean Verb Sense Disambiguation Algorithm Based on Decision Lists of Syntactic Features

  • Kweon Yang Kim
  • Byong Gul Lee
  • Dong Kwon Hong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3043)


We present a new approach for resolving sense ambiguity using the decision lists of syntactic features. This approach exploits the 25 syntactic features including the basic lexical features in the target verb and surrounding words. Our word sense disambiguation algorithm selects the correct sense by utilizing the strongest evidence on the decision lists when the evidence is ranked at the higher level of the decision lists. If the strongest one is not available the contributions of all features that provide weak evidence are summed up and taken into account for the selection. The experiments with ten Korean ambiguous verbs show significant improvement of performance than the decision lists algorithm. In addition, results of experiments show that the syntactic features provide more significant evidences than unordered surrounding words for resolving sense ambiguity.


Target Word Ambiguous Word Training Corpus Word Sense Disambiguation Computational Linguistics 
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 2004

Authors and Affiliations

  • Kweon Yang Kim
    • 1
  • Byong Gul Lee
    • 2
  • Dong Kwon Hong
    • 3
  1. 1.Dept. of Computer EngineeringKyungil UniversityRepublic of Korea
  2. 2.Dept. of Computer Science and EngineeringSeoul Women’s UniversityRepublic of Korea
  3. 3.Dept. of Computer EngineeringKeimyung UniversityRepublic of Korea

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