Paper Title:
Incorporate Syntactic Information for Short Text Classification
  Abstract

As the volume of online short text documents grow tremendously on the Internet, it is much more urgent to solve the task of organizing the short texts well. However, the traditional feature selection methods cannot suitable for the short text. In this paper, we proposed a method to incorporate syntactic information for the short text. It emphasizes the feature which has more dependency relations with other words. The classifier SVM and machine learning environment Weka are involved in our experiments. The experiment results show that incorporate syntactic information in the short text, we can get more powerful features than traditional feature selection methods, such as DF, CHI. The precision of short text classification improved from 86.2% to 90.8%.

  Info
Periodical
Advanced Materials Research (Volumes 268-270)
Edited by
Feng Xiong
Pages
697-700
DOI
10.4028/www.scientific.net/AMR.268-270.697
Citation
R. X. Duan, X. J. Wang, W. F. Li, "Incorporate Syntactic Information for Short Text Classification", Advanced Materials Research, Vols. 268-270, pp. 697-700, 2011
Online since
July 2011
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Price
$32.00
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