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Proceedings Paper

WordSpace: visual summary of text corpora
Author(s): Ulrik Brandes; Martin Hoefer; Jürgen Lerner
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Paper Abstract

In recent years several well-known approaches to visualize the topical structure of a document collection have been proposed. Most of them feature spectral analysis of a term-document matrix with influence values and dimensionality reduction. We generalize this approach by arguing that there are many reasonable ways to project the term-document matrix into low-dimensional space in which different features of the corpus are emphasized. Our main tool is a continuous generalization of adjacency-respecting partitions called structural similarity. In this way we obtain a generic framework in which influence weights in the term-document matrix, dimensionality-reducing projections, and the display of a target subspace may be varied according to nature of the text corpus.

Paper Details

Date Published: 16 January 2006
PDF: 12 pages
Proc. SPIE 6060, Visualization and Data Analysis 2006, 60600N (16 January 2006); doi: 10.1117/12.647867
Show Author Affiliations
Ulrik Brandes, Konstanz Univ. (Germany)
Martin Hoefer, Konstanz Univ. (Germany)
Jürgen Lerner, Konstanz Univ. (Germany)


Published in SPIE Proceedings Vol. 6060:
Visualization and Data Analysis 2006
Robert F. Erbacher; Jonathan C. Roberts; Matti T. Gröhn; Katy Börner, Editor(s)

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