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Conference Paper

Using Restrictive Classification and Meta Classification for Junk Elimination

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons45482

Siersdorfer,  Stefan
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Siersdorfer, S., & Weikum, G. (2005). Using Restrictive Classification and Meta Classification for Junk Elimination. In Advances in information retrieval: 27th European Conference on IR Research, ECIR 2005 (pp. 287-299). Berlin, Germany: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-282F-6
Abstract
This paper addresses the problem of performing supervised classification on document collections containing also junk documents. With junk documents we mean documents that do not belong to the topic categories (classes) we are interested in. This type of documents can typically not be covered by the training set; nevertheless in many real world applications (e.g. classification of web or intranet content, focused crawling etc.) such documents occur quite often and a classifier has to make a decision about them. We tackle this problem by using restrictive methods and ensemble-based meta methods that may decide to leave out some documents rather than assigning them to inappropriate classes with low confidence. Our experiments with four different data sets show that the proposed techniques can eliminate a relatively large fraction of junk documents while dismissing only a significantly smaller fraction of potentially interesting documents.