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Automatic Web Query Classification Using Labeled and Unlabeled Training Data
 

Summary: Automatic Web Query Classification Using Labeled
and Unlabeled Training Data
Steven M. Beitzel, Eric C. Jensen,
Ophir Frieder, David Grossman
Information Retrieval Laboratory
Illinois Institute of Technology
{steve,ej,ophir,dagr}@ir.iit.edu
David D. Lewis, Abdur Chowdhury,
Aleksandr Kolcz
America Online, Inc.
davelewis@daviddlewis.com
{cabdur,arkolcz}@aol.com
ABSTRACT
Accurate topical categorization of user queries allows for
increased effectiveness, efficiency, and revenue potential in
general-purpose web search systems. Such categorization
becomes critical if the system is to return results not just from a
general web collection but from topic-specific databases as well.
Maintaining sufficient categorization recall is very difficult as
web queries are typically short, yielding few features per query.

  

Source: Argamon, Shlomo - Department of Computer Science, Illinois Institute of Technology

 

Collections: Computer Technologies and Information Sciences