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Extracting Semantic Taxonomies of Nouns from a Korean MRD Using a Small Bootstrapping Thesaurus and a Machine Learning Approach

  • SeonHwa Choi
  • HyukRo Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3513)

Abstract

Most approaches for extracting hypernyms of a noun from the definition in an MRD rely on the lexico-syntactic patterns compiled by human experts. Not only these methods require high cost for compiling lexico-syntatic patterns but also it is very difficult for human experts to compile a set of lexical-syntactic patterns with a broad-coverage, because in natural languages there are various different expressions which represent the same concept. To alleviate these problems, this paper proposes a new method for extracting hypernyms of a noun from an MRD. In proposed approach, we use only syntactic(part-of-speech) patterns instead of lexico-syntactic patterns in identifying hypernyms to reduce the number of patterns while keeping their coverage broad. Our experiment shows that the classification accuracy of the proposed method is 92.37% which is significantly much better than those of previous approaches.

Keywords

Function Word Word Sense Disambiguation Computational Linguistics Common Noun Machine Readable Dictionary 
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 2005

Authors and Affiliations

  • SeonHwa Choi
    • 1
  • HyukRo Park
    • 1
  1. 1.Dept. of Computer ScienceChonnam National UniversityGwangjuKorea

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