A New Text Feature Conversion Method for Text Classification

Author

Hu, Minghan ; Liu, Ying ; Wang, Lei ; Ren, Debin

Author_Institution

Coll. of Inf. Sci. & Eng., Northeastern Univ. (NEU), Shenyang, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

62

Lastpage

66

Abstract

Keywords normally carry large amount of category information. In order to fully utilize this kind of information for text classification, this paper proposes a new text feature conversion method based on the SKG model. The method uses the classified texts with the listed key words as the training data to train the classifier. To expand the keyword space, we construct the KWB model and do the text classification by combining the KWB model and the SKG model. The experiment results demonstrate the advantages of this new method.

Keywords

pattern classification; stochastic processes; text analysis; keywords bootstrapping model; stochastic keyword generation model; text classification; text feature conversion method; Educational institutions; Frequency shift keying; Fuzzy systems; Information science; Knowledge engineering; Mutual information; Space technology; Stochastic processes; Text categorization; Training data; Bootstrapping; SKG model; feature conversion; text classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.579

Filename

5358657