Automatic Knowledge Acquisition for Disambiguation and Its' Application to Japanese Noun Phrases

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  • IKEHARA SATORU
    Department of Information and Knowledge Engineering, Faculty of Engineering, Tottori University
  • NAKAI SHINJI
    Department of Information and Knowledge Engineering, Faculty of Engineering, Tottori University
  • MURAKAMI JIN'ICHI
    Department of Information and Knowledge Engineering, Faculty of Engineering, Tottori University

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  • 多義解消のための構造規則の生成方法と日本語名詞句への適用
  • タギ カイショウ ノ タメ ノ コウゾウ キソク ノ セイセイ ホウホウ ト ニホンゴ メイシク エ ノ テキヨウ

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Abstract

In order to represent the knowledge for resolving the syntactical and semantic ambiguities, structural rules and their generalization methods were proposed.In this method, the structural rules are composed of structure definition part and class definition part. The former is written by the set of any of almighty symbol, syntactic attributes, semantic attributes and word itself. From the view point of the number of parameters used for defining the expression structures, the rules are classified into one-dimensional rules, two-dimensional rules and so on, and automatically generated in this order from examples. Prominent feature of this method is in generalization methods. The generated rules are furthermore generalized based on the upper to lower relation of semantic attributes or syntactic attributes to reduce the number of rules without decreasing the performance. This method was applied to generate the dependency rules for Japanese expressions of “A no B no C” which are known as the most popular noun phrases which have hard ambiguities to be resolved. As the result, it was found that structural rules can easily be obtained by this method. The experimental results showed that the dependency relations can be determined at the accuracy of 86% by the rules obtained by this method. This rate is not so low compared to human ability for this kind of ambiguous noun phrases.

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