| Shun-ling Chen|
(Alternative names for this author)
|Co-authors||Alan Shapiro, Amila Akdag Salah, Andrea Scharnhorst, Andrew Famiglietti, Cheng Gao, Chenliang Li, Christian Stegbauer, Conglei Yao, Dan O’Sullivan, Dror Kamir, Edgar Enyedy, Florian Cramer, Gautam John, Geert Lovink, Hans Varghese Mathews, Heather Ford, Hou H., Jiang P., Johanna Niesyto, Joseph M. Reagle, Krzystztof Suchecki, Lawrence Liang, Long Chen, Maja van der Velden, Mark Graham, Matheiu O’Neil, Mayo Fuster Morell, Morgan Currie, Nathaniel Stern, Nathaniel Tkacz, Nicholas Carr, Patrick Lichty, Peter B. Kaufman, R. Stuart Geiger, Scott Kildall, Wang M.|
|Authorship||Publications (2), datasets (0), tools (0)|
|Citations||Total (4), average (2), median (2), max (4), min (0)|
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Shun-ling Chen is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Wiki3C: Exploiting wikipedia for context-aware concept categorization||Context-aware concept categorization
|WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining||English||2013||Wikipedia is an important human generated knowledge base containing over 21 million articles organized by millions of categories. In this paper, we exploit Wikipedia for a new task of text mining: Context-aware Concept Categorization. In the task, we focus on categorizing concepts according to their context. We exploit article link feature and category structure in Wikipedia, followed by introducing Wiki3C, an unsupervised and domain independent concept categorization approach based on context. In the approach, we investigate two strategies to select and filter Wikipedia articles for the category representation. Besides, a probabilistic model is employed to compute the semantic relatedness between two concepts in Wikipedia. Experimental evaluation using manually labeled ground truth shows that our proposed Wiki3C can achieve a noticeable improvement over the baselines without considering contextual information.||0||0|
|Critical Point of View: A Wikipedia Reader||Institute of Network Cultures||English||2011||For millions of internet users around the globe, the search for new knowledge begins with Wikipedia. The encyclopedia’s rapid rise, novel organization, and freely offered content have been marveled at and denounced by a host of commentators. Critical Point of View moves beyond unflagging praise, well-worn facts, and questions about its reliability and accuracy, to unveil the complex, messy, and controversial realities of a distributed knowledge platform.||0||4|