DocumentCode :
2227740
Title :
A new method for query generation applied to learning text classifiers
Author :
Jimmy, Leblet ; Mohamed, Quafafou
Author_Institution :
Nantes Univ., France
fYear :
2003
fDate :
13-17 Oct. 2003
Firstpage :
633
Lastpage :
636
Abstract :
We introduce a new method for query generation. This method uses only a logical approach and does not need a statistical process or a natural language processing. The main interest of this new method is the abstraction. We discuss a method for learning a text classifier and query generation for this classifier. The two problems are resolved in a complementary approach using our query generation method and SVM as text classifiers. We use this approach for studying words polysemy. Our method generates queries in order to retrieve documents about a specific sense of the word and in the same time learning the associated text classifier. Our method have good results.
Keywords :
Boolean functions; classification; data structures; learning (artificial intelligence); query formulation; support vector machines; text analysis; word processing; SVM; data abstraction; document retrieval; query generation; text classifier learning; words polysemy; Boolean functions; Data structures; Natural language processing; Search engines; Support vector machine classification; Support vector machines; Text categorization; Web pages; Web sites;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on
Print_ISBN :
0-7695-1932-6
Type :
conf
DOI :
10.1109/WI.2003.1241284
Filename :
1241284
Link To Document :
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