Implementation of Unsupervised and Supervised Learning Systems for Multilingual Text Categorization

Author

Lee, Chung-Hong ; Yang, Hsin-Chang

Author_Institution

Dept. of Electr. Eng., Nat. Kaohsiung Univ. of Appl. Sci.

fYear

2007

fDate

2-4 April 2007

Firstpage

377

Lastpage

382

Abstract

In this paper we discuss the implementation of the leading supervised and unsupervised approaches for multilingual text categorization. We selected support vector machines (SVM) and latent semantic indexing (LSI) techniques as representatives of supervised and unsupervised methods for system implementation, respectively. The preliminary results show that our platform models including both supervised and unsupervised learning methods have the potentials for multilingual text categorization

Keywords

indexing; support vector machines; text analysis; unsupervised learning; latent semantic indexing; multilingual text categorization; supervised learning systems; support vector machines; unsupervised learning systems; Databases; Humans; Indexing; Information management; Large scale integration; Machine learning; Supervised learning; Support vector machines; Text categorization; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology, 2007. ITNG '07. Fourth International Conference on

Conference_Location

Las Vegas, NV

Print_ISBN

0-7695-2776-0

Type

conf

DOI

10.1109/ITNG.2007.107

Filename

4151713

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2343999