Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/114670
Title: Background net approach for text categorization based on contextual association as term co-occurrence
Authors: Lo, S.-L.
Ding, L. 
Keywords: Acceptance measure
Background net
Personalized article selection
Term association
Text categorization
Issue Date: 2013
Citation: Lo, S.-L.,Ding, L. (2013). Background net approach for text categorization based on contextual association as term co-occurrence. ICIC Express Letters 7 (2) : 369-375. ScholarBank@NUS Repository.
Abstract: Background net previously proposed is a novel approach for capturing and representing background information as a knowledge background accumulated through incremental learning on contextual association of terms in articles. As a continued study on background net, this article further discusses the contextual association as co-occurrence between terms captured on background net. Our theoretical analysis shows that the term association is useful information for representing documents. The experiments on text categorization with representative data sets support this analysis. © 2013 ISSN 1881-803X.
Source Title: ICIC Express Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/114670
ISSN: 1881803X
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

126
checked on Oct 14, 2021

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.