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%.

• Periodical: Advanced Materials Research (Volumes 268-270)
• Pages: 697-700
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.268-270.697
• Online since: July 2011
• Authors: Rui Xue Duan, Xiao Jie Wang, Wen Feng Li
• Keywords: Chi, Dependency Relation, DF, Short Text Classification, Support Vector Machine (SVM)

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