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 <channel rdf:about="http://ebiquity.umbc.edu/tag/language/">
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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/language/</link>
  <description><![CDATA[RSS Tag Search]]></description>
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   <rdf:Seq>
    <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/461/Ensembles-in-Adversarial-Classification-for-Spam"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/466/Finding-Semantic-Web-Ontology-Terms-from-Words"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/261/Integrating-Language-Understanding-Agents-Into-the-Semantic-Web"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/403/Mid-Atlantic-Student-Colloquium-on-Speech-Language-and-Learning"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/246/Grammatical-Inference-Some-of-the-Questions-Out-There"/>
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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>
 </image>
 <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/461/Ensembles-in-Adversarial-Classification-for-Spam">
  <title><![CDATA[Ensembles in Adversarial Classification for Spam]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/461/Ensembles-in-Adversarial-Classification-for-Spam</link>
  <description><![CDATA[The standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. As the spammers change their tactics, the performance of such classifiers tends to decrease over time. Gathering and labeling more data to periodically retrain the classifier is expensive. We present a method based on an ensemble of classifiers that can detect when its performance might be degrading and retrain itself, all without manual intervention.  Experiments wit...]]></description>
  <dc:date>2009-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/466/Finding-Semantic-Web-Ontology-Terms-from-Words">
  <title><![CDATA[Finding Semantic Web Ontology Terms from Words]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/466/Finding-Semantic-Web-Ontology-Terms-from-Words</link>
  <description><![CDATA[The Semantic Web was designed to unambiguously define and use ontologies to encode data and knowledge on the Web. Many people find it difficult, however, to write complex RDF statements and queries because it requires familiarity with the appropriate ontologies and the terms they define. We describe a framework that eases the experiences in authoring and querying RDF data, in which we focus on automatically finding a set of appropriate Semantic Web ontology terms from a set of words used as t...]]></description>
  <dc:date>2009-10-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis">
  <title><![CDATA[Delta TFIDF: An Improved Feature Space for Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis</link>
  <description><![CDATA[Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using t...]]></description>
  <dc:date>2009-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading">
  <title><![CDATA[Cross-Document Coreference Resolution: A Key Technology for Learning by Reading]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading</link>
  <description><![CDATA[Automatic knowledge base population from text is an important technology for a broad range of approaches to learning by reading. Effective automated knowledge base population depends critically upon coreference resolution of entities across sources. Use of a wide range of features, both those that capture evidence for entity merging and those that argue against merging, can significantly improve machine learning-based cross-document coreference resolution.  Results from the Global Entity Dete...]]></description>
  <dc:date>2009-03-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/261/Integrating-Language-Understanding-Agents-Into-the-Semantic-Web">
  <title><![CDATA[Integrating Language Understanding Agents Into the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/261/Integrating-Language-Understanding-Agents-Into-the-Semantic-Web</link>
  <description><![CDATA[Many intelligent agents need knowledge and information to
support their reasoning and problem solving. The World
Wide Web is a vast, open, accessible and free source of
knowledge, but virtually all of it is encoded as natural language
text � a form difficult for most agents to directly understand.
We describe initial work on adapting a mature language
understanding agent to process Web text and publish
its output in the SemanticWeb language OWL. This approach
adds knowledge on the W...]]></description>
  <dc:date>2005-11-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/403/Mid-Atlantic-Student-Colloquium-on-Speech-Language-and-Learning">
  <title><![CDATA[Mid-Atlantic Student Colloquium on Speech, Language and Learning]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/403/Mid-Atlantic-Student-Colloquium-on-Speech-Language-and-Learning</link>
  <description><![CDATA[The First Mid-Atlantic Student Colloquium on Speech, Language and Learning is a one-day event to be held at the Johns Hopkins University in Baltimore on Friday, 23 September 2011.  Its goal is to bring together students taking computational approaches to speech, language, and learning, so that they can introduce their research to the local student community, give and receive feedback, and engage each other in collaborative discussion.  Attendance is open to all and free but space is limited, ...]]></description>
  <dc:date>2011-09-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild.  But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy?  For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream.  The problem of responding to such changes when they are known to have occurred has been studied extensively.  In...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/246/Grammatical-Inference-Some-of-the-Questions-Out-There">
  <title><![CDATA[Grammatical Inference: Some of the Questions Out There]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/246/Grammatical-Inference-Some-of-the-Questions-Out-There</link>
  <description><![CDATA[Grammatical Inference is a field concerned with learning
grammars given data about a language.  In this talk we
survey some of the questions being addressed by researchers
in the field.  Some of these are now classical and have been
looked into for some time, others are more recent:

understanding the models and the paradigms:
what does polynomial language learning mean?

learning more complex families of languages

scaling up and using grammatical inference in applications]]></description>
  <dc:date>2008-06-10</dc:date>
 </item>
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