Improving the classification accuracy of automatic text processing systems using context vectors and back-propagation algorithms

Author

Farkas, Jennifer

Author_Institution

Centre for Inf. Technol. Innovation, Ind. Canada, Laval, Que., Canada

Volume

2

fYear

1996

fDate

26-29 May 1996

Firstpage

696

Abstract

We analyze some of the benefits of combining the context-vector representation of documents with the back-propagation paradigm for document classification. We discuss an implementation of this architecture, called NeuroFile, which combines automatic document classification with similarity-based, as well as Boolean retrieval facilities in a single electronic filing system. The quality of performance of NeuroFile is compared with an earlier system called NeuroClass. We show that NeuroFile achieves a 9% classification improvement over NeuroClass

Keywords

backpropagation; classification; document image processing; feedforward neural nets; information retrieval; word processing; Boolean retrieval facilities; NeuroClass; NeuroFile; automatic document classification; automatic text processing systems; backpropagation algorithms; classification accuracy; context vectors; context-vector representation; documents; electronic filing system; performance; similarity based retrieval facilities; Algorithm design and analysis; Electronic mail; Information analysis; Information technology; Libraries; Neural networks; Pattern recognition; Technological innovation; Text processing; Thesauri;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 1996. Canadian Conference on

Conference_Location

Calgary, Alta.

ISSN

0840-7789

Print_ISBN

0-7803-3143-5

Type

conf

DOI

10.1109/CCECE.1996.548248

Filename

548248