Abstract
We address the problem of categorizing a large set of linked documents with important content and structure aspects, in particular, from the Wikipedia collection proposed at the INEX 2009 XML Mining challenge. We analyze the network of collection pages and turn it into valuable features for the classification. We combine the content-based and link-based features of pages to train an accurate categorizer for unlabelled pages. In the multi-label setting, we revise a number of existing techniques and test some which show a good scalability. We report evaluation results obtained with a variety of learning methods and techniques on the training set of the Wikipedia corpus.
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Chidlovskii, B. (2010). Multi-label Wikipedia Classification with Textual and Link Features. In: Geva, S., Kamps, J., Trotman, A. (eds) Focused Retrieval and Evaluation. INEX 2009. Lecture Notes in Computer Science, vol 6203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14556-8_38
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DOI: https://doi.org/10.1007/978-3-642-14556-8_38
Publisher Name: Springer, Berlin, Heidelberg
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