Automatic learning of a concept relation dictionary for a text mining system

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  • テキストマイニングシステム向けの構造抽出ルールの自動学習
  • テキストマイニング システム ムケ ノ コウゾウ チュウシュツ ルール ノ ジドウ ガクシュウ

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Abstract

A text mining method using domain-dependent dictionaries can classify text data with various viewpoints. The method uses a key concept dictionary, which stores important words and phrases for domains. Also, the method uses a concept relation dictionary, which is a rule set consisted of their combination. In the method, the knowledge dictionaries are very important and give a strong influence to classification results. However, we have to generate the dictionaries through trial and error. It is difficult to apply the method to many tasks. In this paper, we try to learn a concept relation dictionary automatically. The method extracts key concepts using lexical analysis from text data, generates training examples from the concepts and their classes given by a human expert, and applies the examples to a fuzzy inductive learning algorithm, IDF. Also, the paper shows the method acquires an appropriate rule set by numerical experiments based on 10-fold cross validation and using more than 1, 000 daily business reports.

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