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  <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>
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  <dc:date>2010-03-09</dc:date>
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  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
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  <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>
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