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mySENSEVAL: Explaining WSD System Performance Using Target Word Features

  • Harri M. T. Saarikoski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3513)

Abstract

Word sense disambiguation (WSD) is an unsolved problem in NLP. The field has produced a variety of methods but none of them potent enough to reach high, human-tagger accuracy in demanding NLP applications. Our contribution to WSD is mySENSEVAL, an error analyzer using SENSEVAL evaluation scores (in mySQL database) to find significant correlations between WSD system types and lexico-conceptual features (from WordNet and SUMO).

Keywords

Target Word Word Sense Disambiguation Lexical Resource Feature Typology Evaluation Workshop 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Harri M. T. Saarikoski
    • 1
  1. 1.Department of General Linguistics, KIT Language Technology Doctorate SchoolHelsinki UniversityHelsinkiFinland

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