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Neighborhood Conscious Hypertext Categorization

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44021

Angelova,  Ralitsa
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, 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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Zitation

Angelova, R. (2004). Neighborhood Conscious Hypertext Categorization. Master Thesis, Universität des Saarlandes, Saarbrücken.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0027-F483-0
Zusammenfassung
A fundamental issue in statistics, pattern recognition, and machine learning is that of classification. In a traditional classification problem, we wish to assign one of k labels (or classes) to each of n objects (or documents), in a way that is consistent with some observed data available about that problem. For achieving better classification results, we try to capture the information derived by pairwise realtionships between objects, in particular hyperlinks between web documents. the usage of hyperlinks poses new problems not addressed in the extensive text classification literature. Links contain high quality seantic clues that a purely text-based classifier can not take advantage of. However, exploiting link inoframtion is non-trivial because it is noisy and a naive use of terms in the link neghborhood of a document can degrade accuracy. The problem becomes even harder when only a very small fraction of document labels ar known to the classifier and can be used for training, as it is the case in a real classification scenario. Our work is based on an algorithm proposed by Soumen Chakrabarti and uses the theory of Markov Random Fields to derive a relaxation labelling technique for the class assignment problem. We show that the extra information contaned in the hyperlinks between the documents can be explited to achieve significant improvement in the performance of classification. We implemente our algorithm in Java and ran our experiments on to sets of data obtained from the DBLP and IMDB databases. We oberved up to 5.5 improvement in the accuracy of the classification and up the 10 higher recall and precision resultls.