Abstract
The demo presents a prototype - called TopicExplorer - that combines topic modeling, key word search and visualization techniques to explore a large collection of Wikipedia documents. Topics derived by Latent Dirichlet Allocation are presented by top words. In addition, topics are accompanied by image thumbnails extracted from related Wikipedia documents to aid sense making of derived topics during browsing. Topics are shown in a linear order such that similar topics are close. Topics are mapped to color using that order. The auto-completion of search terms suggests words together with their color coded topics, which allows to explore the relation between search terms and topics. Retrieved documents are shown with color coded topics as well. Relevant documents and topics found during browsing can be put onto a shortlist. The tool can recommend further documents with respect to the average topic mixture of the shortlist. © 2012 Springer-Verlag.
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CITATION STYLE
Hinneburg, A., Preiss, R., & Schröder, R. (2012). TopicExplorer: Exploring document collections with topic models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7524 LNAI, pp. 838–841). https://doi.org/10.1007/978-3-642-33486-3_59
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