DMCA
Hierarcical document classification using . . . (2005)
Cached
Download Links
BibTeX
@MISC{Li05hierarcicaldocument,
author = {Tao Li and Mitsunori Ogihara and Shenghuo Zhu},
title = {Hierarcical document classification using . . . },
year = {2005}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Automated text categorization has witnessed a booming interest with the ex-ponential growth of information and the ever-increasing needs for organizations. The underlying hierarchical structure identifies the relationships of dependence between different categories and provides valuable sources of information for categorization. Although considerable research has been conducted in the field of hierarchical document categorization, little has been done on automatic generation of topic hierarchies. In this paper, we propose the method of using linear discriminant projection to generate more meaningful intermediate levels of hierarchies in large flat sets of classes. The linear discriminant projection approach first transforms all documents onto a low-dimensional space and then clusters the categories into hierarchies accordingly. The paper also investigates the effect of using generated hierarchical structure for text classification. Our experiments show that generated hierarchies improve classification performance in most cases.
Keyphrases
hierarcical document classification ever-increasing need generated hierarchy different category valuable source low-dimensional space underlying hierarchical structure hierarchical document categorization automated text categorization ex-ponential growth topic hierarchy classification performance automatic generation linear discriminant projection large flat set considerable research text classification linear discriminant projection approach booming interest meaningful intermediate level generated hierarchical structure
