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New Technology of Library and Information Service  2008, Vol. 24 Issue (8): 24-30    DOI: 10.11925/infotech.1003-3513.2008.08.04
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A Research on the Methodological of Text Visualization
Zhao Qi 1,2  Zhang Zhixiong1  Sun Tan1
1 (National Science Library, Chinese Academy of Sciences, Beijing 100190, China)
2 (Graduate University of Chinese Academy of Sciences, Beijing 100049, China)
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Abstract  

Text visualization is a method which uses computer technology to make a graphical show of the specific text resources. This paper analyzes the current text visualization characteristics through analysis of the typical text visualization system. There are four different classes of text visualization, including based on vocabulary, based on article, based on time series, based on topic which reflects the main text visualization techniques. The final part is about how text visualization used in the information environment now.

Key wordsText Visualization      Knowledge Expression      Topic Discovery     
Received: 16 June 2008      Published: 25 August 2008
: 

G250.76

 
Corresponding Authors: Zhao Qi     E-mail: zhaoqi@mail.las.ac.cn
About author:: Zhao Qi,Zhang Zhixiong,Sun Tan

Cite this article:

Zhao Qi,Zhang Zhixiong,Sun Tan. A Research on the Methodological of Text Visualization. New Technology of Library and Information Service, 2008, 24(8): 24-30.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2008.08.04     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2008/V24/I8/24

[1] Wise J A, Pennock K,  Lantrip D, et al. Visualizing the Non-visual:Spatial Analysis and Interaction with Information from Text Documents [C]. Proceedings on Information Visualization 1995.
[2] Mladenic M G D. Visualization of News Articles [EB/OL].[2008-06-12]. http://eprints.pascal-network.org/archive/00000742/01/GrobelnikMladenic-Contexter.pdf.
[3] Hearst M A. TileBars:Visualization of Term Distribution Information in Full Text Information Access[C]. In:Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1995:59-66.
[4] TileBars Examples [EB/OL].[2008-06-12]. http://people.ischool.berkeley.edu/~hearst/images/tb-example.html.
[5] Weber W. Text Visualization-What Colors Tell About a Text[C]. In:Proceedings of the 11th International Conference Information Visualization, 2007:354-362.
[6] Leskovec J,Grobelnik M, Milic-Frayling N. Learning Sub-structures of Document Semantic Graphs for Document Summarization[C]. LinkKDD. 2004.
[7] Paley W B. TextArc:Showing Word Frequency and Distribution in Text[C]. IEEE Symposium on Information Visualization. 2002.
[8] TextArc - An Alternate Way to View a Text [EB/OL].[2008-06-12]. http://textarc.org/.
[9] Grobelnik M,Mladenic D. Text-Garden——Text-Mining Software Tools [EB/OL].[2008-06-12]. http://kt.ijs.si/Dunja/textgarden/.
[10] Havre S, Hetzler B, Nowell L. ThemeRiverTM:In Search of Trends, Patterns, and Relationships [EB/OL].[2008-06-12]. http://infoviz.pnl.gov/pdf/themeriver99.pdf.
[11] Plaisant C, Mushin R, Snyder A, et al. LifeLines:Using Visualization to Enhance Navigation and Analysis of Patient Records[J]. The Craft of Information Visualization:Readings and Reflections, 2003. 1(1):1-5.
[12] Battiato S, Gianpiero Di Blasi, Gallo G, et al. Theme Mountain:a SVG-based Visual Data Mining Tool [EB/OL].[2008-06-12]. http://www.svgopen.org/2005/papers/ThemeMountain/.
[13] History Flow - Visualizing the Editing History of Wikipedia Pages [EB/OL].[2008-06-12]. http://www.research.ibm.com/visual/projects/history_flow/.
[14] Viégas F B,  Wattenberg M,  Dave K. Studying Cooperation and Conflict Between Authors with History Flow Visualizations[C]. In:Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2004:575-582.
[15] Krishnan M, Bohn S, Cowley W, et al. Scalable Visual Analytics of Massive Textual Datasets[C] In:Parallel and Distributed Processing Symposium, IPDPS. 2007.
[16] IN-SPIRETM Visual Document Analysis [EB/OL].[2008-06-12]. http://in-spire.pnl.gov/.
[17] Aureka ThemeScape 9.2 User Guide [EB/OL].[2008-06-12]. http://aureka.micropat.com/7w/html/customer_support/documentation/user_guides/themepublisherug.pdf.
[18] Miller N E, Wong P C, Brewster M, et al. TOPIC ISLANDSTM-A Wavelet-Based Text Visualization System[C]. In:Visualization’98. Proceedings. 1998:189-196.
[19] Rushall D A, Ilgen M R. DEPICT:Documents Evaluated as Pictures. Visualizing Information Using Context Vectors and Self-organizing Maps[C]. In:Proceedings of the 1996 IEEE Symposium on Information Visualization (INFOVIS ’96). 1996, IEEE Computer Society.

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