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  5. 	<meta name="description" content="CSDM 2011 is a workshop at WSDM 2011 examining crowdsourcing for search and data mining.">
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  10. 	<title>CSDM 2011 | Crowdsourcing for Search and Data Mining | a WSDM 2011 workshop</title>
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  18. 	<a href="http://www.wsdm2011.org/"><h1>WSDM 2011 - Hong Kong, China</h1></a>
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  19. 	<img src="title.jpg" width="600" height="299" alt="CSDM 2011 - Crowdsourcing for Search and Data Mining" />
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  24. <li><a href="index.html">Overview</a></li> 
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  25. <li><a href="people.html">People</a></li> 
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  26. <li><a href="sponsors.html">Sponsors</a></li>  
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  27. <li><a href="papers.html">Call for Papers</a></li>
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  28. <li><a href="program.html">Program</a></li>  
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  29. <li><a href="proceedings.html">Proceedings</a></li>  
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  34. <h2>Email list</h2>
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  35. Subscribe to Google group for workshop announcements and discussion.
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  37. Email address: <input type=text name=email>
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  41. <a href="http://groups.google.com/group/csdm-2011">Visit the CSDM 2011 Google group website</a>
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  44. <p><b> Wednesday February 9, 8:30am-5pm, Hong Kong</b> (see <a href="http://www.wsdm2011.org/wsdm2011/overview">overview</a>)
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  46. <p>The advent of <a href="http://en.wikipedia.org/wiki/Crowdsourcing">crowdsourcing</a> is revolutionizing data annotation, evaluation, and other traditionally manual-labor intensive processes by dramatically reducing the time, cost, and effort involved. This in turn is driving a disruptive shift in search and data mining methodology in areas such as:</p>
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  48. <li><p><strong>Evaluation</strong>: the Cranfield paradigm for search evaluation requires manually assessing document relevance to search queries. Recent work on stochastic evaluation has reduced but not removed this need for manual assessment.</p></li>
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  49. <li><p><strong>Supervised Learning</strong>: while traditional costs associated with data annotation have driven recent machine learning work (e.g. Learning to Rank) toward greater use of unsupervised and semi-supervised methods, the emergence of crowdsourcing has made labeled data far easier to acquire, thereby driving a potential resurgence in fully-supervised methods.</p></li>
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  50. <li><strong>Applications:</strong> Crowdsourcing has introduced exciting new opportunities to integrate human labor into automated systems: handling difficult cases where automation fails, exploiting the breadth of backgrounds, geographic dispersion, real-time response, etc.</li>
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  52. CSDM 2011 will build on the recent success of the <a href="http://ir.ischool.utexas.edu/cse2010">Crowdsourcing for Search Evaluation Workshop (CSE 2010)</a> at <a href="http://www.sigir2010.org">SIGIR 2010</a>, extending beyond search evaluation to broadly explore development and application of crowdsourcing techniques for search and data mining. 
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  54. <h2>Audience and Scope</h2>
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  55. This workshop will bring together researchers and practitioners of crowdsourcing techniques.
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  56. This meeting is intended to work as a catalyst for future collaborations, as well as
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  57. one of the main forums for sharing the latest developments in this area. The workshop will
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  58. provide participants an opportunity to hear about and discuss key issues such as:
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  61. <li>Advantages/disadvantages of crowdsourcing vs. traditional methods</li>
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  62. <li>When and how to use crowdsourcing for an experiment</li>
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  63. <li>How to increase quality and throughput of crowdsourcing</li>
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  64. <li>How to detect cheating and handle noise in crowdsourcing</li>
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  65. <li>General guidelines and best practices of crowdsourcing experiments</li>
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  66. <li>The latest improvements and current state-of-the-art crowdsourcing systems and methods</li>
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  67. <li>The reach and potential of recent innovative applications in the area
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  73. <a href="mailto:nrejack@gmail.com">Contact webmaster</a> |  © CSDM 2011 | Photo by <a href="http://www.flickr.com/photos/hamedog/">Hamedog</a>, used under Creative Commons</div>
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