Publikation: WordSpace Visual Summary of Text Corpora
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2006
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ERBACHER, Robert F., ed., Jonathan C. ROBERTS, ed., Matti T. GRÖHN, ed., Katy BÖRNER, ed.. Visualization and Data Analysis 2006. SPIE, 2006, 60600N. SPIE Proceedings. 6060. Available under: doi: 10.1117/12.647867
Zusammenfassung
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.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Information Retrieval, Text Visualization, Data Analysis, Graph Drawing
Konferenz
Electronic Imaging 2006, San Jose, CA
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ISO 690
BRANDES, Ulrik, Martin HOEFER, Jürgen LERNER, 2006. WordSpace Visual Summary of Text Corpora. Electronic Imaging 2006. San Jose, CA. In: ERBACHER, Robert F., ed., Jonathan C. ROBERTS, ed., Matti T. GRÖHN, ed., Katy BÖRNER, ed.. Visualization and Data Analysis 2006. SPIE, 2006, 60600N. SPIE Proceedings. 6060. Available under: doi: 10.1117/12.647867BibTex
@inproceedings{Brandes2006-01-15WordS-5810,
year={2006},
doi={10.1117/12.647867},
title={WordSpace Visual Summary of Text Corpora},
number={6060},
publisher={SPIE},
series={SPIE Proceedings},
booktitle={Visualization and Data Analysis 2006},
editor={Erbacher, Robert F. and Roberts, Jonathan C. and Gröhn, Matti T. and Börner, Katy},
author={Brandes, Ulrik and Hoefer, Martin and Lerner, Jürgen},
note={Article Number: 60600N}
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<dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
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