Search (3 results, page 1 of 1)

  • × author_ss:"Klavans, J.L."
  1. Muresan, S.; Klavans, J.L.: Inducing terminologies from text : a case study for the consumer health domain (2013) 0.00
    0.0025239778 = product of:
      0.010095911 = sum of:
        0.010095911 = weight(_text_:information in 682) [ClassicSimilarity], result of:
          0.010095911 = score(doc=682,freq=4.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.16457605 = fieldWeight in 682, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=682)
      0.25 = coord(1/4)
    
    Abstract
    Specialized medical ontologies and terminologies, such as SNOMED CT and the Unified Medical Language System (UMLS), have been successfully leveraged in medical information systems to provide a standard web-accessible medium for interoperability, access, and reuse. However, these clinically oriented terminologies and ontologies cannot provide sufficient support when integrated into consumer-oriented applications, because these applications must "understand" both technical and lay vocabulary. The latter is not part of these specialized terminologies and ontologies. In this article, we propose a two-step approach for building consumer health terminologies from text: 1) automatic extraction of definitions from consumer-oriented articles and web documents, which reflects language in use, rather than relying solely on dictionaries, and 2) learning to map definitions expressed in natural language to terminological knowledge by inducing a syntactic-semantic grammar rather than using hand-written patterns or grammars. We present quantitative and qualitative evaluations of our two-step approach, which show that our framework could be used to induce consumer health terminologies from text.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.727-744
  2. Klavans, J.L.; LaPlante, R.; Golbeck, J.: Subject matter categorization of tags applied to digital images from art museums (2014) 0.00
    0.0017847219 = product of:
      0.0071388874 = sum of:
        0.0071388874 = weight(_text_:information in 1175) [ClassicSimilarity], result of:
          0.0071388874 = score(doc=1175,freq=2.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.116372846 = fieldWeight in 1175, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=1175)
      0.25 = coord(1/4)
    
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.1, S.3-12
  3. Huang, X.; Soergel, D.; Klavans, J.L.: Modeling and analyzing the topicality of art images (2015) 0.00
    0.0014872681 = product of:
      0.0059490725 = sum of:
        0.0059490725 = weight(_text_:information in 2127) [ClassicSimilarity], result of:
          0.0059490725 = score(doc=2127,freq=2.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.09697737 = fieldWeight in 2127, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2127)
      0.25 = coord(1/4)
    
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.8, S.1616-1644