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 :
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