DocumentCode
3376812
Title
A hybrid architecture for text classification
Author
Register, Michael S. ; Kannan, Narasimhan
Author_Institution
Digital Equipment Corp., Colorado Springs, CO, USA
fYear
1992
fDate
10-13 Nov 1992
Firstpage
286
Lastpage
292
Abstract
SKIS, a prototype system that allows for the construction and use of text classification applications, is discussed. SKIS uses a combination of knowledge-based techniques, statistical techniques, morphological processing, and relevance feedback learning techniques to perform text classification. SKIS has been used to construct a prototype text classification application for the routing of customer service requests within customer support centers. The SKIS run-time architecture, the development and knowledge maintenance environment, and how SKIS is used are described. The benefits of combining knowledge-based and statistical techniques for text classification are discussed. SKIS is compared with other text classification systems
Keywords
document handling; knowledge based systems; natural languages; SKIS; customer service requests; customer support centers; knowledge maintenance environment; knowledge-based techniques; morphological processing; relevance feedback learning techniques; run-time architecture; statistical techniques; text classification; Application software; Customer service; Feedback; Natural languages; Prototypes; Routing; Runtime; Springs; Text categorization; Text processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools with Artificial Intelligence, 1992. TAI '92, Proceedings., Fourth International Conference on
Conference_Location
Arlington, VA
Print_ISBN
0-8186-2905-3
Type
conf
DOI
10.1109/TAI.1992.246417
Filename
246417
Link To Document