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      <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/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing"/>
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 <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>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing">
  <title><![CDATA[A Hybrid Approach to Unsupervised Relation Discovery via Linguistic Analysis, Entropy-based Label Ranking and Semantic Typing]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing</link>
  <description><![CDATA[Zareen Syed will talk about "A Hybrid Approach to Unsupervised Relation Discovery via Linguistic Analysis, Entropy-based Label Ranking and Semantic Typing" 

ABSTRACT:
There are today two main approaches in Information Extraction systems to extract entities and relations between them from text: a knowledge engineering approach which requires grammars to be hand crafted to express the rules for the system, a quite laborious process; an automatic training approach which requires the hand ann...]]></description>
  <dc:date>2009-10-27</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/577/Improving-Word-Similarity-by-Augmenting-PMI-with-Estimates-of-Word-Polysemy">
  <title><![CDATA[Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/577/Improving-Word-Similarity-by-Augmenting-PMI-with-Estimates-of-Word-Polysemy</link>
  <description><![CDATA[Pointwise mutual information (PMI) is a widely used word similarity measure, but it lacks a clear explanation of how it works. We explore how PMI differs from distributional similarity, and we introduce a novel metric, PMImax, that augments PMI with information about a word's number of senses. The coefficients of PMImax are determined empirically by maximizing a utility function based on the performance of automatic thesaurus generation. We show that it outperforms traditional PMI in the appl...]]></description>
  <dc:date>2013-06-01</dc:date>
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  <title><![CDATA[Finding Appropriate Semantic Web Ontology Terms from Words]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/457/Finding-Appropriate-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 doing so requires familiarity with the appropriate ontologies and the terms they deﬁne. We describe a system that automatically maps a set of ordinary English words to a set of appropriate ontology terms on the Semantic Web. We use the Swoogle Semantic Web search engine to provide ontol...]]></description>
  <dc:date>2009-05-01</dc:date>
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