Difference between revisions of "2010-03-25: CFP: ACM Transactions on Intelligent Systems and Technology (ACM TIST) Special Issue on Intelligent Visual Interfaces for Text Analysis"

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Most of the world's information is contained in the form of text documents. To help people cope with the ever increasing amounts of text information, researchers have developed a wide array of text analysis technologies. Complex analysis results of these technologies, however, may be difficult and non-trivial for average users to digest and leverage. To help users better interpret text analysis results and discover opportunities to improve the results, researchers have been integrating text analytics technologies with interactive visualization technologies. In the past, most work focused on using basic visualization (e.g., bar charts and pie charts) to present final analysis results or inventing visual metaphors to illustrate simple analysis results (e.g., tf–idf measure of keywords). This special issue is aimed at highlighting state-of-the-art technologies and systems that tightly integrate advanced text analytics with innovative use of interactive visualization to maximize the value of both. We solicit articles that explore, define, and develop intelligent visual interfaces that help enhance the consumption and quality of advanced text analysis results.  We specifically look for technologies or tools that can: 1) better convey and explain complex and abstract text analytic results and make them consumable; 2) compensate for the deficiencies of current text analysis technologies; and 3) help discover opportunities for improving text analytics to support an iterative, progressive analytic process.
 
Most of the world's information is contained in the form of text documents. To help people cope with the ever increasing amounts of text information, researchers have developed a wide array of text analysis technologies. Complex analysis results of these technologies, however, may be difficult and non-trivial for average users to digest and leverage. To help users better interpret text analysis results and discover opportunities to improve the results, researchers have been integrating text analytics technologies with interactive visualization technologies. In the past, most work focused on using basic visualization (e.g., bar charts and pie charts) to present final analysis results or inventing visual metaphors to illustrate simple analysis results (e.g., tf–idf measure of keywords). This special issue is aimed at highlighting state-of-the-art technologies and systems that tightly integrate advanced text analytics with innovative use of interactive visualization to maximize the value of both. We solicit articles that explore, define, and develop intelligent visual interfaces that help enhance the consumption and quality of advanced text analysis results.  We specifically look for technologies or tools that can: 1) better convey and explain complex and abstract text analytic results and make them consumable; 2) compensate for the deficiencies of current text analysis technologies; and 3) help discover opportunities for improving text analytics to support an iterative, progressive analytic process.
 
Topics of interest include but not limited to:<br>
 
Topics of interest include but not limited to:<br>
•    Novel, intelligent interfaces for text analytics<br>
+
* Novel, intelligent interfaces for text analytics<br>
•    Visual and interactive text analytics<br>
+
* Visual and interactive text analytics<br>
•    Collaborative visual text analytics<br>
+
* Collaborative visual text analytics<br>
•    Scalable visual text analytics<br>
+
* Scalable visual text analytics<br>
•    Visual representations and interaction techniques to allow users to see, explore, and understand large amounts of text information <br>
+
* Visual representations and interaction techniques to allow users to see, explore, and understand large amounts of text information <br>
•    Techniques to support production, presentation, and dissemination of the analysis results to a variety of audiences<br>
+
* Techniques to support production, presentation, and dissemination of the analysis results to a variety of audiences<br>
•    Data representations and transformations that convert conflicting and dynamic text data in ways that support visualization and analysis<br>
+
* Data representations and transformations that convert conflicting and dynamic text data in ways that support visualization and analysis<br>
•    User studies concerning intelligent interfaces for text analysis<br>
+
* User studies concerning intelligent interfaces for text analysis<br>
•    Evaluation methods for text analysis techniques and systems<br>
+
* Evaluation methods for text analysis techniques and systems<br>
  
 
==On-Line Submission==
 
==On-Line Submission==

Revision as of 15:15, 25 March 2010


Most of the world's information is contained in the form of text documents. To help people cope with the ever increasing amounts of text information, researchers have developed a wide array of text analysis technologies. Complex analysis results of these technologies, however, may be difficult and non-trivial for average users to digest and leverage. To help users better interpret text analysis results and discover opportunities to improve the results, researchers have been integrating text analytics technologies with interactive visualization technologies. In the past, most work focused on using basic visualization (e.g., bar charts and pie charts) to present final analysis results or inventing visual metaphors to illustrate simple analysis results (e.g., tf–idf measure of keywords). This special issue is aimed at highlighting state-of-the-art technologies and systems that tightly integrate advanced text analytics with innovative use of interactive visualization to maximize the value of both. We solicit articles that explore, define, and develop intelligent visual interfaces that help enhance the consumption and quality of advanced text analysis results. We specifically look for technologies or tools that can: 1) better convey and explain complex and abstract text analytic results and make them consumable; 2) compensate for the deficiencies of current text analysis technologies; and 3) help discover opportunities for improving text analytics to support an iterative, progressive analytic process. Topics of interest include but not limited to:

  • Novel, intelligent interfaces for text analytics
  • Visual and interactive text analytics
  • Collaborative visual text analytics
  • Scalable visual text analytics
  • Visual representations and interaction techniques to allow users to see, explore, and understand large amounts of text information
  • Techniques to support production, presentation, and dissemination of the analysis results to a variety of audiences
  • Data representations and transformations that convert conflicting and dynamic text data in ways that support visualization and analysis
  • User studies concerning intelligent interfaces for text analysis
  • Evaluation methods for text analysis techniques and systems

On-Line Submission

http://mc.manuscriptcentral.com/tist (please select “Intelligent Visual Interfaces for Text Analysis” as the manuscript type)
Details of the journal and manuscript preparation are available on the website: http://tist.acm.org/
Each paper will be peer-reviewed by at least three reviewers.

Important Dates

Full Paper Submission Deadline: June 30, 2010
Review Notification: August 15th, 2010
Final Manuscript: October 15th, 2010
Publication Date: December 2010

Guest Editors
Shixia Liu, IBM China Research Lab
http://domino.research.ibm.com/comm/research_people.nsf/pages/lsx.index.html

Michelle X. Zhou, IBM Almaden Research Center
http://www.research.ibm.com/RIA/People/Zhou/Zhou.htm

Giuseppe Carenini, University of British Columbia
http://www.cs.ubc.ca/~carenini/index.html

Huamin Qu, Hong Kong University of Science and Technology
http://www.huamin.org/