DocumentCode :
1750741
Title :
Using rough sets to construct sense type decision trees for text categorization
Author :
Bleyberg, Maria Zamfir ; Elumalai, Arulkumar
Author_Institution :
Comput. & Inf. Sci. Dept., Kansas State Univ., Manhattan, KS, USA
Volume :
1
fYear :
2001
fDate :
25-28 July 2001
Firstpage :
19
Abstract :
Accurate text categorization is needed for efficient and effective text retrieval, search and filtering. Finding appropriate categories and manually assigning them to existing documents is very laborious. The paper shows a simple procedure for automatic extraction of atomic sense types (semantic categories) from documents based on rough sets. The atomic sense types are nodes of a sense type decision tree, which represents a taxonomy
Keywords :
decision trees; information retrieval; rough set theory; text analysis; atomic sense types; automatic extraction; rough sets; semantic categories; sense type decision tree; sense type decision trees; taxonomy; text categorization; text filtering; text retrieval; text search; Data mining; Decision trees; Information filtering; Information filters; Information retrieval; Natural languages; Pressing; Rough sets; Taxonomy; Text categorization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
Type :
conf
DOI :
10.1109/NAFIPS.2001.944220
Filename :
944220
Link To Document :
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