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 <channel rdf:about="http://ebiquity.umbc.edu/tag/twitter/">
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  <image rdf:resource="http://ebiquity.umbc.edu/img/logo.jpg" />  <title><![CDATA[RSS Tag Search]]></title>
  <link>http://ebiquity.umbc.edu/tag/twitter/</link>
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    <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/81/Twitterment"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/499/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/500/Clustering-short-status-messages-A-topic-model-based-approach"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/367/Why-We-Twitter-Understanding-Microblogging-Usage-and-Communities"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data"/>
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    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/346/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data"/>
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 <image rdf:about="http://ebiquity.umbc.edu/img/logo.jpg">
  <title>UMBC ebiquity research group</title>
  <link>http://ebiquity.umbc.edu</link>
  <url>http://ebiquity.umbc.edu/img/logo.jpg</url>
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 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/81/Twitterment">
  <title><![CDATA[Twitterment]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/81/Twitterment</link>
  <description><![CDATA[Twitterment is a search engine for the Twitter microblogging system.]]></description>
  <dc:date>2007-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/499/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/paper/html/id/499/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[In recent years, social media outlets such as Twitter and Facebook have drawn attention from companies and researchers interested in detecting trends. The informal nature of status updates from these services leads to a higher volume of updates, because each update takes little care to generate, but each update is usually short and noisy (misspellings, lack of punctuation, non-standard abbreviations and capitalization). These shortcomings cause traditional Natural Language Processing (NLP) te...]]></description>
  <dc:date>2010-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/500/Clustering-short-status-messages-A-topic-model-based-approach">
  <title><![CDATA[Clustering short status messages: A topic model based approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/500/Clustering-short-status-messages-A-topic-model-based-approach</link>
  <description><![CDATA[Recently, there has been an exponential rise in the use of online social media systems like Twitter and Facebook. Even more usage has been observed during events related to natural disasters, political turmoil or other such crises. Tweets or status messages are short and may not carry enough contextual clues. Hence, applying traditional natural language processing algorithms on such data is challenging. Topic model is a popular method for modeling term frequency occurrences for documents in a...]]></description>
  <dc:date>2010-07-17</dc:date>
 </item>
 <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 towards the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTur...]]></description>
  <dc:date>2010-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/367/Why-We-Twitter-Understanding-Microblogging-Usage-and-Communities">
  <title><![CDATA[Why We Twitter: Understanding Microblogging Usage and Communities]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/367/Why-We-Twitter-Understanding-Microblogging-Usage-and-Communities</link>
  <description><![CDATA[Microblogging is a new form of communication in which
users can describe their current status in short posts distributed
by instant messages, mobile phones, email or the
Web. Twitter, a popular microblogging tool has seen a lot
of growth since it launched in October, 2006. In this paper,
we present our observations of the microblogging phenomena
by studying the topological and geographical properties
of Twitter’s social network. We find that people use microblogging
to talk about th...]]></description>
  <dc:date>2007-08-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text">
  <title><![CDATA[Coarse and Fine Grained Sentiment Analysis of Online Text]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text</link>
  <description><![CDATA[Sentiment analysis - the automated extraction of expressions of positive and negative attitudes from text - has received a great amount of attention over the last ten years. Over the same period, via the widespread growth in the use of what we have come to call social media, there has been an explosion in the amount of publically available user generated text on the Web. This text has the potential of providing a source of real time, time tagged sentiments from people all over the globe.

T...]]></description>
  <dc:date>2010-05-11</dc:date>
 </item>
 <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>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach">
  <title><![CDATA[Clustering short status messages: a topic model based approach]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/355/Clustering-short-status-messages-a-topic-model-based-approach</link>
  <description><![CDATA[Recently, there has been an exponential rise in the use of online social media systems like Twitter and Facebook. Even more usage has been observed during events related to natural disasters, political turmoil or other such crises. Tweets or status messages are short and may not carry enough contextual clues. Hence, applying traditional natural language processing algorithms on such data is challenging. Topic model is a popular method for modeling term frequency occurrences for documents in a...]]></description>
  <dc:date>2010-07-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/346/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/event/html/id/346/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[Master's Thesis Defense

In recent years, social media outlets such as Twitter and Facebook have drawn attention from companies and researchers interested in detecting trends.  The informal nature of status updates from these services leads to a higher volume of updates, because each update takes little care to generate, but each update is usually short and noisy (misspellings, lack of punctuation, non-standard abbreviations and capitalization).  These shortcomings cause traditional Natural...]]></description>
  <dc:date>2010-05-19</dc:date>
 </item>
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