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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=word+similarity">
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/621/UMBC_EBIQUITY-CORE-Semantic-Textual-Similarity-Systems"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources">
  <title><![CDATA[Robust Semantic Text Similarity Using LSA, Machine Learning and Linguistic Resources]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources</link>
  <description><![CDATA[Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM~2013~and SemEval-2014~tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines Latent Semantic Analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of tex...]]></description>
  <dc:date>2016-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/621/UMBC_EBIQUITY-CORE-Semantic-Textual-Similarity-Systems">
  <title><![CDATA[UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/621/UMBC_EBIQUITY-CORE-Semantic-Textual-Similarity-Systems</link>
  <description><![CDATA[We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the corresponding three runs. All of them used a word similarity feature that combined LSA word similarity and WordNet knowledge. The first run, which achieved the top mean score on the task of all the submissions, used a simple term alignment algorithm. The other two runs, ranked second and fourth, used SVM models to combine a larger sets of features.]]></description>
  <dc:date>2013-06-13</dc:date>
 </item>
 <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>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/558/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/558/Improving-Word-Similarity-by-Augmenting-PMI-with-Estimates-of-Word-Polysemy</link>
  <description><![CDATA[Although pointwise mutual information (PMI) has become a commonly used word similarity measure, a clear understanding of how it works has been lacking.  In this paper 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 ...]]></description>
  <dc:date>2011-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/348/Word-and-phrase-similarity">
  <title><![CDATA[Word and phrase similarity]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/348/Word-and-phrase-similarity</link>
  <description><![CDATA[Computing semantic similarity between words/phrases has important applications in natural language processing, information retrieval, and artificial intelligence. There are two prevailing approaches to computing word similarity, based on either using of a thesaurus (e.g., WordNet ) or statistics from a large corpus. We provide a hybrid approach combining the two methods that is demonstrated on a web site through two services: one that returns a similarity score for two words or phrases and an...]]></description>
  <dc:date>2013-01-09</dc:date>
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