Acquiring Concept Hierarchies of Adjectives from Corpora: Towards Construction of Ontology of Adjectives from a real data

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  • KANZAKI KYOKO
    National Institute of Information and Communications Technology
  • MA QING
    National Institute of Information and Communications Technology Faculty of Science and Technology, Ryukoku University
  • YAMAMOTO EIKO
    National Institute of Information and Communications Technology Graduate School of Engineering, Kobe University
  • SHIRADO TAMOTSU
    National Institute of Information and Communications Technology
  • ISAHARA HITOSHI
    National Institute of Information and Communications Technology Graduate School of Engineering, Kobe University

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Other Title
  • コーパスからの形容詞概念階層の構築と評価―実データによる形容詞オントロジーの構築にむけて一
  • コーパス カラノ ケイヨウシ ガイネン カイソウ ノ コウチク ト ヒョウカ ジツデータ ニ ヨル ケイヨウシ オントロジー ノ コウチク ニ ムケテ
  • Towards Construction of Ontology of Adjectives from a real data
  • 実データによる形容詞オントロジーの構築にむけて

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Abstract

The method of organizing word meanings is a crucial issue with lexical databases.Weare aiming to extract the semantic structure of concepts of adjectives from corporaautomatically.The first step to achieving this is to obtain the concepts of adjec-tives from corpora, for which we used abstract nouns.We constructed linguistic databy extracting semantic relations between abstract nouns and adjectives from corpusdata.This paper describes how to hierarchically organize abstract concepts of adjec-tives mainly using the Complementary Similarity Measure (CSM) which calculatesinclusion relations (hypernym/hyponym relations) between words.To estimate hy-pernym/hyponym relations between words, we compared three hierarchical structuresof abstract concepts of adjectives: according to CSM, CSM with frequency (Freq), and an alternative similarity measure based on coefficient overlap.We evaluated au-tomatically generated concept hierarchies of adjectives with those in EDR, and foundthat 43% of those automatically generated were better than EDR.

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