The instructional design of Chinese text classification based on SVM

Author

Sichao Wei ; Jianyi Guo ; Zhengtao Yu ; Peng Chen ; Yantuan Xian

Author_Institution

Sch. of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China

fYear

2013

fDate

25-27 May 2013

Firstpage

5114

Lastpage

5117

Abstract

In order to resolve the comprehension difficulties of theory and implementation about Chinese text classification in " The principle and application of pattern recognition" curriculum for graduate students, this paper introduces the experiment of Chinese text classification into teaching practice. According to the text classification characteristics, we design the experiment scheme about Chinese text classification based on SVM, using word frequency statistics to extract feature and SVM classification algorithm, using vector space model to construct the feature space of text classification. So that readers can deeply understand and master the theoretical knowledge through the open the link, then expand on this basis.

Keywords

computer science education; feature extraction; natural language processing; pattern classification; statistics; support vector machines; teaching; text analysis; Chinese text classification characteristics; SVM classification algorithm; feature extraction; feature space; graduate students; instructional design; pattern recognition application; pattern recognition principle; teaching practice; vector space model; word frequency statistics; Classification algorithms; Feature extraction; Support vector machine classification; Text categorization; Training; Vectors; feature selection; support vector machine algorithm; teaching experiment; text classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2013 25th Chinese

Conference_Location

Guiyang

Print_ISBN

978-1-4673-5533-9

Type

conf

DOI

10.1109/CCDC.2013.6561863

Filename

6561863