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Web-Scale Classification: Classifying Big Data from the Web, WSDM 2014 Workshop
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URL: http://lshtc.iit.demokritos.gr/WSDM_WS
Workshop at WSDM 2014, Crowne Plaza Times Square, New York City, February 28, 2014
Scope
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The huge amount of data available in the Web in various forms (text, images, videos etc.) raises challenging and difficult problems concerning the extraction and assessment of useful information. Intelligent systems for knowledge extraction are nowadays of utmost importance due to the scale of the data in the Web. A key module for any intelligent system is the capability of identifying and classifying correctly data items in a pre-defined set of classes. In order to ease the classification and organization of the data many real world systems make use of taxonomies over the set of categories which are typically organized in a hierarchical structure with parent-child relations. Typical examples of such taxonomies are DMOZ, http://www.dmoz.org/, the International Patent Classification, http://www.wipo.int/classifications/ipc/en/ or Wikipedia.
In this context, new challenges arise on classification and clustering problems, for web scale applications dealing with millions of categories: data sparsity and class imbalance at different levels of the hierarchy remain open issues for this setting; the class statistical dependencies raise opportunities for new learning approaches; controlling the classifier complexity or the inference budget becomes critical.
The goal of this workshop is to discuss and assess recent research focusing on classification and mining for Web-scale category systems. In particular, we want to attract researchers developing new ways to exploit such Web-scale systems, e.g. by exploring how different category systems can be combined (through multi-task or transfer learning for example) to improve classification accuracy or by exploring how hierarchies can be refined or simplified for classification and mining purposes. We also want to attract research work that reveals new properties of large scale category systems, e.g. the type of data distributions in large scale systems. The following topics are of interest to the workshop (this list is not exhaustive):
* Semi-supervised learning for WSC
* Transfer learning for WSC
* Multi-task learning for WSC
* Deep learning approaches to WSC
* Clustering and hierarchy refinement
* Mining large scale hierarchical category systems
* Large scale classification for e-commerce
* Budget learning for large scale classification and clustering
* Parallel implementations of large scale classification and clustering systems
Submmisions
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Submissions must be written in English, following the ACM guidelines (http://www.acm.org/sigs/publications/proceedings-templates). We encourage both long papers (6 pages max) as well as short papers (4 pages max) including references and figures. The Easychair electronic submission system will be used for the papers (https://www.easychair.org/conferences/?conf=wscbd2014). Please, refer to the workshop page for details about the submission format and process.
Important dates
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* Paper submission - December 23
* Notification - January 10
* Camera ready paper - January 20
* Workshop - February 28
Organizers
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Massih-Reza Amini, LIG, Grenoble, France
Ion Androutsopoulos, AUEB, Athens, Greece
Thierry Artières, LIP6, Paris, France
Patrick Gallinari, LIP6, Paris, France
Eric Gaussier, LIG, Grenoble, France
George Paliouras, NCSR "Demokritos", Athens, Greece
Ioannis Partalas, LIG, Grenoble, France
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