Search (7 results, page 1 of 1)

  • × author_ss:"Oard, D.W."
  • × year_i:[2010 TO 2020}
  1. Oard, D.W.; Webber, W.: Information retrieval for e-discovery (2013) 0.01
    0.0053541656 = product of:
      0.021416662 = sum of:
        0.021416662 = weight(_text_:information in 211) [ClassicSimilarity], result of:
          0.021416662 = score(doc=211,freq=18.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.34911853 = fieldWeight in 211, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=211)
      0.25 = coord(1/4)
    
    Abstract
    E-discovery refers generally to the process by which one party (for example, the plaintiff) is entitled to discover evidence in the form of electronically stored information that is held by another party (for example, the defendant), and that is relevant to some matter that is the subject of civil litigation (that is, what is commonly called a "lawsuit"). Information Retrieval for E-Discovery describes the emergence of the field, identifies the information retrieval issues that arise, reviews the work to date on this topic, and summarizes major open issues. Information Retrieval for E-Discovery is an ideal primer for anyone with an interest in e-discovery; be it researchers who first practiced law but now study information retrieval, or those who studied information retrieval but now practice law.
    Content
    Table of contents 1. Introduction 2. The E-Discovery Process 3. Information Retrieval for E-Discovery 4. Evaluating E-Discovery 5. Experimental Evaluation 6. Looking to the Future 7. Conclusion A. Interpreting Legal Citations Acknowledgments Notations and Acronyms References
    Series
    Foundations and trends(r) in information retrieval; 7,2/3
  2. Wang, J.; Oard, D.W.: Matching meaning for cross-language information retrieval (2012) 0.00
    0.004164351 = product of:
      0.016657405 = sum of:
        0.016657405 = weight(_text_:information in 7430) [ClassicSimilarity], result of:
          0.016657405 = score(doc=7430,freq=8.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.27153665 = fieldWeight in 7430, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=7430)
      0.25 = coord(1/4)
    
    Abstract
    This article describes a framework for cross-language information retrieval that efficiently leverages statistical estimation of translation probabilities. The framework provides a unified perspective into which some earlier work on techniques for cross-language information retrieval based on translation probabilities can be cast. Modeling synonymy and filtering translation probabilities using bidirectional evidence are shown to yield a balance between retrieval effectiveness and query-time (or indexing-time) efficiency that seems well suited large-scale applications. Evaluations with six test collections show consistent improvements over strong baselines.
    Source
    Information processing and management. 48(2012) no.4, S.631-653
  3. Kim, S.; Ko, Y.; Oard, D.W.: Combining lexical and statistical translation evidence for cross-language information retrieval (2015) 0.00
    0.0035694437 = product of:
      0.014277775 = sum of:
        0.014277775 = weight(_text_:information in 1606) [ClassicSimilarity], result of:
          0.014277775 = score(doc=1606,freq=8.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.23274569 = fieldWeight in 1606, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=1606)
      0.25 = coord(1/4)
    
    Abstract
    This article explores how best to use lexical and statistical translation evidence together for cross-language information retrieval (CLIR). Lexical translation evidence is assembled from Wikipedia and from a large machine-readable dictionary, statistical translation evidence is drawn from parallel corpora, and evidence from co-occurrence in the document language provides a basis for limiting the adverse effect of translation ambiguity. Coverage statistics for NII Testbeds and Community for Information Access Research (NTCIR) queries confirm that these resources have complementary strengths. Experiments with translation evidence from a small parallel corpus indicate that even rather rough estimates of translation probabilities can yield further improvements over a strong technique for translation weighting based on using Jensen-Shannon divergence as a term-association measure. Finally, a novel approach to posttranslation query expansion using a random walk over the Wikipedia concept link graph is shown to yield further improvements over alternative techniques for posttranslation query expansion. Evaluation results on the NTCIR-5 English-Korean test collection show statistically significant improvements over strong baselines.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.23-39
  4. Cheng, A.-S.; Fleischmann, K.R.; Wang, P.; Ishita, E.; Oard, D.W.: ¬The role of innovation and wealth in the net neutrality debate : a content analysis of human values in congressional and FCC hearings (2012) 0.00
    0.003091229 = product of:
      0.012364916 = sum of:
        0.012364916 = weight(_text_:information in 276) [ClassicSimilarity], result of:
          0.012364916 = score(doc=276,freq=6.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.20156369 = fieldWeight in 276, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=276)
      0.25 = coord(1/4)
    
    Abstract
    Net neutrality is the focus of an important policy debate that is tied to technological innovation, economic development, and information access. We examine the role of human values in shaping the Net neutrality debate through a content analysis of testimonies from U.S. Senate and FCC hearings on Net neutrality. The analysis is based on a coding scheme that we developed based on a pilot study in which we used the Schwartz Value Inventory. We find that the policy debate surrounding Net neutrality revolves primarily around differences in the frequency of expression of the values of innovation and wealth, such that the proponents of Net neutrality more frequently invoke innovation, while the opponents of Net neutrality more frequently invoke wealth in their prepared testimonies. The paper provides a novel approach for examining the Net neutrality debate and sheds light on the connection between information policy and research on human values.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1360-1373
  5. Kang, H.; Plaisant, C.; Elsayed, T.; Oard, D.W.: Making sense of archived e-mail : exploring the Enron collection with NetLens (2010) 0.00
    0.0025239778 = product of:
      0.010095911 = sum of:
        0.010095911 = weight(_text_:information in 3446) [ClassicSimilarity], result of:
          0.010095911 = score(doc=3446,freq=4.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.16457605 = fieldWeight in 3446, 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=3446)
      0.25 = coord(1/4)
    
    Abstract
    Informal communications media pose new challenges for information-systems design, but the nature of informal interaction offers new opportunities as well. This paper describes NetLens-E-mail, a system designed to support exploration of the content-actor network in large e-mail collections. Unique features of NetLens-E-mail include close coupling of orientation, specification, restriction, and expansion, and introduction and incorporation of a novel capability for iterative projection between content and actor networks within the same collection. Scenarios are presented to illustrate the intended employment of NetLens-E-mail, and design walkthroughs with two domain experts provide an initial basis for assessment of the suitability of the design by scholars and analysts.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.4, S.723-744
  6. Gao, N.; Dredze, M.; Oard, D.W.: Person entity linking in email with NIL detection (2017) 0.00
    0.0014872681 = product of:
      0.0059490725 = sum of:
        0.0059490725 = weight(_text_:information in 3830) [ClassicSimilarity], result of:
          0.0059490725 = score(doc=3830,freq=2.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.09697737 = fieldWeight in 3830, 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=3830)
      0.25 = coord(1/4)
    
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.10, S.2412-2424
  7. La Fata, C.M.; Lupo, T.; Oard, D.W.: ¬A combined fuzzy-SEM evaluation approach to identify the key drivers of the academic library service quality in the digital technology era : an empirical study (2017) 0.00
    0.0014872681 = product of:
      0.0059490725 = sum of:
        0.0059490725 = weight(_text_:information in 3831) [ClassicSimilarity], result of:
          0.0059490725 = score(doc=3831,freq=2.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.09697737 = fieldWeight in 3831, 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=3831)
      0.25 = coord(1/4)
    
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.10, S.2425-2438