Search (3 results, page 1 of 1)

  • × author_ss:"Kilicoglu, H."
  1. Keselman, A.; Rosemblat, G.; Kilicoglu, H.; Fiszman, M.; Jin, H.; Shin, D.; Rindflesch, T.C.: Adapting semantic natural language processing technology to address information overload in influenza epidemic management (2010) 0.00 
    0.0022374375 = product of:
  0.004474875 = sum of:
    0.004474875 = product of:
      0.00894975 = sum of:
        0.00894975 = weight(_text_:a in 1312) [ClassicSimilarity], result of:
          0.00894975 = score(doc=1312,freq=14.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1685276 = fieldWeight in 1312, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1312)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The explosion of disaster health information results in information overload among response professionals. The objective of this project was to determine the feasibility of applying semantic natural language processing (NLP) technology to addressing this overload. The project characterizes concepts and relationships commonly used in disaster health-related documents on influenza pandemics, as the basis for adapting an existing semantic summarizer to the domain. Methods include human review and semantic NLP analysis of a set of relevant documents. This is followed by a pilot test in which two information specialists use the adapted application for a realistic information-seeking task. According to the results, the ontology of influenza epidemics management can be described via a manageable number of semantic relationships that involve concepts from a limited number of semantic types. Test users demonstrate several ways to engage with the application to obtain useful information. This suggests that existing semantic NLP algorithms can be adapted to support information summarization and visualization in influenza epidemics and other disaster health areas. However, additional research is needed in the areas of terminology development (as many relevant relationships and terms are not part of existing standardized vocabularies), NLP, and user interface design.
    Type
    a
  2. Deardorff, A.; Masterton, K.; Roberts, K.; Kilicoglu, H.; Demner-Fushman, D.: ¬A protocol-driven approach to automatically finding authoritative answers to consumer health questions in online resources (2017) 0.00 
    0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 3688) [ClassicSimilarity], result of:
          0.006765375 = score(doc=3688,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 3688, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3688)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The purpose of this research was to establish an upper bound on finding answers to health-related questions in MedlinePlus and other online resources. Seven reference librarians tested a set of protocols to determine whether it was possible to use the types and foci of the questions extracted from customer requests submitted to the National Library of Medicine to find authoritative answers to these questions. Librarians tested the protocols manually to determine if the process was sufficiently robust and accurate to later automate. Results indicated that the extracted terms provide enough information to find authoritative answers for about 60% of questions and that certain question types are more likely to result in authoritative answers than others. The question corpus and analysis performed for this project will inform automatic question answering systems, and could lead to suggestions for new content to include in MedlinePlus. This approach can serve as an example to researchers interested in methods of evaluating question answering tools and the contents of online databases.
    Type
    a
  3. Humphrey, S.M.; Rogers, W.J.; Kilicoglu, H.; Demner-Fushman, D.; Rindflesch, T.C.: Word sense disambiguation by selecting the best semantic type based on journal descriptor indexing : preliminary experiment (2006) 0.00 
    0.0016571716 = product of:
  0.0033143433 = sum of:
    0.0033143433 = product of:
      0.0066286866 = sum of:
        0.0066286866 = weight(_text_:a in 4912) [ClassicSimilarity], result of:
          0.0066286866 = score(doc=4912,freq=12.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12482099 = fieldWeight in 4912, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=4912)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    An experiment was performed at the National Library of Medicine® (NLM®) in word sense disambiguation (WSD) using the Journal Descriptor Indexing (JDI) methodology. The motivation is the need to solve the ambiguity problem confronting NLM's MetaMap system, which maps free text to terms corresponding to concepts in NLM's Unified Medical Language System® (UMLS®) Metathesaurus®. If the text maps to more than one Metathesaurus concept at the same high confidence score, MetaMap has no way of knowing which concept is the correct mapping. We describe the JDI methodology, which is ultimately based an statistical associations between words in a training set of MEDLINE® citations and a small set of journal descriptors (assigned by humans to journals per se) assumed to be inherited by the citations. JDI is the basis for selecting the best meaning that is correlated to UMLS semantic types (STs) assigned to ambiguous concepts in the Metathesaurus. For example, the ambiguity transport has two meanings: "Biological Transport" assigned the ST Cell Function and "Patient transport" assigned the ST Health Care Activity. A JDI-based methodology can analyze text containing transport and determine which ST receives a higher score for that text, which then returns the associated meaning, presumed to apply to the ambiguity itself. We then present an experiment in which a baseline disambiguation method was compared to four versions of JDI in disambiguating 45 ambiguous strings from NLM's WSD Test Collection. Overall average precision for the highest-scoring JDI version was 0.7873 compared to 0.2492 for the baseline method, and average precision for individual ambiguities was greater than 0.90 for 23 of them (51%), greater than 0.85 for 24 (53%), and greater than 0.65 for 35 (79%). On the basis of these results, we hope to improve performance of JDI and test its use in applications.
    Type
    a

Authors

Years

Themes