Search (2 results, page 1 of 1)

  • × author_ss:"Oard, D.W."
  • × theme_ss:"Computerlinguistik"
  1. Oard, D.W.; He, D.; Wang, J.: User-assisted query translation for interactive cross-language information retrieval (2008) 0.00 
    3.9466174E-4 = product of:
  0.005919926 = sum of:
    0.005919926 = product of:
      0.011839852 = sum of:
        0.011839852 = weight(_text_:information in 2030) [ClassicSimilarity], result of:
          0.011839852 = score(doc=2030,freq=8.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.23274569 = fieldWeight in 2030, 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=2030)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    Interactive Cross-Language Information Retrieval (CLIR), a process in which searcher and system collaborate to find documents that satisfy an information need regardless of the language in which those documents are written, calls for designs in which synergies between searcher and system can be leveraged so that the strengths of one can cover weaknesses of the other. This paper describes an approach that employs user-assisted query translation to help searchers better understand the system's operation. Supporting interaction and interface designs are introduced, and results from three user studies are presented. The results indicate that experienced searchers presented with this new system evolve new search strategies that make effective use of the new capabilities, that they achieve retrieval effectiveness comparable to results obtained using fully automatic techniques, and that reported satisfaction with support for cross-language searching increased. The paper concludes with a description of a freely available interactive CLIR system that incorporates lessons learned from this research.
    Source
    Information processing and management. 44(2008) no.1, S.181-211
  2. Kim, S.; Ko, Y.; Oard, D.W.: Combining lexical and statistical translation evidence for cross-language information retrieval (2015) 0.00 
    3.9466174E-4 = product of:
  0.005919926 = sum of:
    0.005919926 = product of:
      0.011839852 = sum of:
        0.011839852 = weight(_text_:information in 1606) [ClassicSimilarity], result of:
          0.011839852 = score(doc=1606,freq=8.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = 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)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    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

Authors

Years