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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/690/The-OceanLink-Project">
  <title><![CDATA[The OceanLink Project]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/690/The-OceanLink-Project</link>
  <description><![CDATA[Today's scientific investigations are producing large numbers of scholarly products. These products continue to increase in diversity and complexity as researchers recognize that scholarly achievements are not only published articles but also datasets, software, and associated supporting materials.  OceanLink is an online platform that addresses scholarly discovery and collaboration in the ocean sciences. The OceanLink project leverages Semantic Web technologies, web mining, and crowdsourcing...]]></description>
  <dc:date>2014-12-07</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/572/Social-and-Semantic-Computing-in-Support-of-Citizen-Science">
  <title><![CDATA[Social and Semantic Computing in Support of Citizen Science]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/572/Social-and-Semantic-Computing-in-Support-of-Citizen-Science</link>
  <description><![CDATA[We describe our ongoing work on using social media as a platform for citizen science. Building on our previous work of facilitating citizen science observations, and using RDF to integrate them with existing biodiversity knowledge, we are currently building Facebook Apps that will enable the reporting of observations, as well as the browsing and tagging of existing observations.  The tagging capability serves two main purposes. First, it permits (and, we hope, encourages) multi-stage crowdsou...]]></description>
  <dc:date>2011-08-05</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing">
  <title><![CDATA[Annotating named entities in Twitter data with crowdsourcing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing</link>
  <description><![CDATA[We describe our experience using both Amazon Mechanical Turk (MTurk) and Crowd Flower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally been the focus of named entity experiments, Twitter is far more informal and abbreviated. The collected annotations and annotation techniques will provide a first step toward the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTurk...]]></description>
  <dc:date>2010-06-06</dc:date>
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  <title><![CDATA[crowdsourcing research data]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/291/crowdsourcing-research-data</link>
  <dc:date>2010-03-09</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data">
  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[We present a system for improving the accuracy of one NLP technique, Named Entity Recognition or NER, on Twitter data by training a recognizer specifically for this type of data.  This training data is obtained from the Amazon Mechanical Turk crowdsourcing platform.]]></description>
  <dc:date>2010-05-08</dc:date>
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