W. Yi and C. Meek, “Improving Similarity Measures for Short Segments of Text,” Proceedings of 22nd conference on Artifical Intellignce (AAAI-07), Vancouver, 24-26 July 2007, pp. 1489-1494.

has been cited by the following article:

• TITLE: Short Text Classification Based on Improved ITC

  AUTHORS: Liangliang Li, Shouning Qu

  KEYWORDS: ITC; Text Classification; Short Text

  JOURNAL NAME: Journal of Computer and Communications, Vol.1 No.4, October 18, 2013

  ABSTRACT: The long text classification has got great achievements, but short text classification still needs to be perfected. In this paper, at first, we describe why we select the ITC feature selection algorithm not the conventional TFIDF and the superiority of the ITC compared with the TFIDF, then we conclude the flaws of the conventional ITC algorithm, and then we present an improved ITC feature selection algorithm based on the characteristics of short text classification while combining the concepts of the Documents Distribution Entropy with the Position Distribution Weight. The improved ITC algorithm conforms to the actual situation of the short text classification. The experimental results show that the performance based on the new algorithm was much better than that based on the traditional TFIDF and ITC.