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<!--
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=text+corpus">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=text+corpus]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for text corpus]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/344/A-New-Approach-for-Automatic-Thesaurus-Generation"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/622/KELVIN-a-tool-for-automated-knowledge-base-construction"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/351/UMBC-webbase-corpus"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia">
  <title><![CDATA[PowerRelations: A Question Answering System for DBPedia]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/393/PowerRelations-A-Question-Answering-System-for-DBPedia</link>
  <description><![CDATA[Large amounts of structured and semi-structured semantic data are available on the Web. A well-known example is DBpedia, which extracts data from Wikipedia, encodes it in the Semantic Web language RDF, and stores it in a triplestore. Although a formal query language, SPARQL, is available for accessing such data, it remains challenging for users to query the knowledge unless they are familiar with SPARQL and the particular ontologies used. We have developed an intuitive system for users to ex...]]></description>
  <dc:date>2011-04-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/344/A-New-Approach-for-Automatic-Thesaurus-Generation">
  <title><![CDATA[A New Approach for Automatic Thesaurus Generation]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/344/A-New-Approach-for-Automatic-Thesaurus-Generation</link>
  <description><![CDATA[Distributional similarity has long been used to determine  how similar two words are and has been used in automatic thesaurus generation. Such distribution similarity measures, however, do not always work well for finding synonyms in a text corpus because synonyms may not necessarily have the most similar contexts. We have developed a novel alternative approach in automatic thesaurus generation using pointwise mutual information (PMI) and by exploiting co-occurrence patterns of synonyms, whic...]]></description>
  <dc:date>2010-05-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/622/KELVIN-a-tool-for-automated-knowledge-base-construction">
  <title><![CDATA[KELVIN: a tool for automated knowledge base construction]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/622/KELVIN-a-tool-for-automated-knowledge-base-construction</link>
  <description><![CDATA[We present KELVIN, an automated system for processing a large text corpus and distilling a knowledge base about persons, organizations, and locations. We have tested the KELVIN system on several corpora, including: (a) the TAC KBP 2012 Cold Start corpus, which consists of public Web pages from the University of Pennsylvania, and (b) a subset of 26k news articles taken from English Gigaword 5th edition.  Our NAACL HLT 2013 demonstration permits a user to interact with a set of searchable HTML ...]]></description>
  <dc:date>2013-06-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet">
  <title><![CDATA[Automatic Discovery of Semantic Relations using MindNet]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet</link>
  <description><![CDATA[Information extraction deals with extracting entities (such as people,organizations or locations) and named relations between entities (such as "People born-in Country") from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new “model” to extract semantic relations fully automatically from text using the Encarta encyclope...]]></description>
  <dc:date>2010-05-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/351/UMBC-webbase-corpus">
  <title><![CDATA[UMBC webbase corpus]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/351/UMBC-webbase-corpus</link>
  <description><![CDATA[The UMBC webBase corpus (http://ebiq.org/r/351) is a dataset containing a collection of English paragraphs with over  three billion words processed from the February 2007 crawl from the  Stanford WebBase project (http://bit.ly/WebBase).  Compressed, it is about 13GB in size.

It was derived from  the February 2007 crawl, which is one of the
largest collections and contains 100 million web
pages from more than 50,000 websites. The Stanford WebBase project did an excellent job in extrac...]]></description>
  <dc:date>2013-04-09</dc:date>
 </item>
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