Abstract
We address the problem of categorizing a large set of linked documents with important content and structure aspects, for example, from Wikipedia collection proposed at the INEX XML Mining track. We cope with the case where there is a small number of labeled pages and a very large number of unlabeled ones. Due to the sparsity of the link based structure of Wikipedia, we apply the spectral and graph-based techniques developed in the semi-supervised machine learning. We use the content and structure views of Wikipedia collection to build a transductive categorizer for the unlabeled pages. We report evaluation results obtained with the label propagation function which ensures a good scalability on sparse graphs.
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Chidlovskii, B. (2009). Semi-supervised Categorization of Wikipedia Collection by Label Expansion. In: Geva, S., Kamps, J., Trotman, A. (eds) Advances in Focused Retrieval. INEX 2008. Lecture Notes in Computer Science, vol 5631. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03761-0_42
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DOI: https://doi.org/10.1007/978-3-642-03761-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03760-3
Online ISBN: 978-3-642-03761-0
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