An evaluation method of words tendency using decision tree

  • Authors:

  • El-Sayed Atlam;Masaki Oono;Jun-ichi Aoe

  • Affiliations:

  • University of Tokushima, Tokushima, Japan;University of Tokushima, Tokushima, Japan;University of Tokushima, Tokushima, Japan

  • Venue:
  • ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
  • Year:
  • 2003

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Abstract

In every text, some words have frequency appearance and are considered as keywords because they have strong relationship with the subjects of their texts, these words frequencies change with time-series variation in a given period. However, in traditional text dealing methods and text search techniques, the importance of frequency change with time-series variation is not considered. Therefore, traditional methods could not correctly determine index of word's popularity in a given period. In this paper, a new method is proposed to estimate automatically the stability classes (increasing, relatively constant, and decreasing) that indicate word's popularity with time-series variation based on the frequency change in past texts data. At first, learning data was produced by defining four attributes to measure frequency change of word quantitatively, these four attributes were extracted automatically from electronic texts.According to the comparison between the evaluation of the decision tree results and manually (Human) results, F-measures of increasing, relatively constant and decreasing classes were 0.847, 0.851, and 0.768 respectively, and the effectiveness of this method is achieved.