DocumentCode
1629304
Title
Categorization of news articles using neural text categorizer
Author
Jo, Taeho
Author_Institution
Inha Univ., Incheon, South Korea
fYear
2009
Firstpage
19
Lastpage
22
Abstract
This research proposes the application of NTC (neural text categorizer) for categorizing news articles. Even if the research on text categorization has been progressed very much, documents should be still encoded into numerical vectors. Encoding so causes the two main problems: huge dimensionality and sparse distribution. The idea of this research as the solution to the problems is to encode documents into string vectors and apply the NTC as a string vector based approach to text categorization. The idea will be described in detail and validated.
Keywords
data mining; neural nets; text analysis; word processing; neural text categorizer; news articles categorization; string vector encoding; Data mining; Encoding; Machine learning; Machine learning algorithms; Natural languages; Nearest neighbor searches; Neural networks; Support vector machines; Testing; Text categorization;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location
Jeju Island
ISSN
1098-7584
Print_ISBN
978-1-4244-3596-8
Electronic_ISBN
1098-7584
Type
conf
DOI
10.1109/FUZZY.2009.5277330
Filename
5277330
Link To Document