Abstract
This paper proposes a novel text mining method for any given document set. It is based on PageRank-based centrality scores within the graph structure generated from the similarity of all document pairs. Evaluations using a newspaper collection show that the proposed approach yields much better performance in terms of main topic identification and topical clustering than the baseline method. Furthermore, we show an example of document set visualization that offers novel document browsing through the topic structure. Experiments show that our topic structure mining method is useful for user-oriented document selection.
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Toda, H., Fujimura, K., Kataoka, R., Kitagawa, H. (2006). Topic Structure Mining Using PageRank Without Hyperlinks. In: Sugimoto, S., Hunter, J., Rauber, A., Morishima, A. (eds) Digital Libraries: Achievements, Challenges and Opportunities. ICADL 2006. Lecture Notes in Computer Science, vol 4312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11931584_18
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DOI: https://doi.org/10.1007/11931584_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49375-4
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