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Microwave-shielded polar molecules
Non-Hermitian topology and braiding with photonic crystals
物理系colloquium: 超快激光精密制造
Remarks on fluctuations in large N dynamics
报告题目:
TextFlow: Better Understanding of Evolving Topics in Text
 报告人:
刘世霞
微软亚洲研究院
报告时间:
2012-05-06 11:00
报告地点:
FIT 楼1区315房间
主办单位:
软件学院
  简介:
TextFlow: Better Understanding of Evolving Topics in Text
 
报告人:    刘世霞, 微软亚洲研究院
报告时间:  2012年5月6日星期日11:00am~12:00am
报告地点:  清华大学FIT 楼1区315房间
主办单位:  清华大学软件学院计算机图形学与辅助设计研究所
 
Dr. Shixia Liu is a lead researcher in the Internet Graphics Group at Microsoft Research Asia. She received a B.S. and M.S. in Computational Mathematics from Harbin Institute of Technology, a Ph.D. in Computer Aided Design and Computer Graphics from Tsinghua University. Before she joined MSRA, She worked as a research staff member and research manager at IBM China Research Lab, where she managed the departments of Smart Visual Analytics and User Experience. Her research interests include interactive, visual text analytics and interactive, visual network analysis. She is the program co-chair of VINCI'2012. She was in the program committee of PacificVis, ACM Multimedia, SDM, and IUI, VINCI, IVAPP, and the guest editor of ACM Transactions on Intelligent Systems and Technology, and Tsinghua Science and Technology. She has authored and coauthored 40+ papers in refereed journals and conferences, and filed 40+ patents.
 
Abstract: Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. This talk will introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. This work first extends an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the hidden keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively.
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