Title :
Categorization of news articles using neural text categorizer
Author_Institution :
Inha Univ., Incheon, South Korea
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;
Conference_Titel :
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location :
Jeju Island
Print_ISBN :
978-1-4244-3596-8
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2009.5277330
