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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Toda, H., Fujimura, K., Kataoka, R., & Kitagawa, H. (2006). Topic structure mining using pageRank without hyperlinks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4312 LNCS, pp. 151–162). Springer Verlag. https://doi.org/10.1007/11931584_18
Mendeley helps you to discover research relevant for your work.