Search (2 results, page 1 of 1)

  • × author_ss:"He, D."
  • × theme_ss:"Multilinguale Probleme"
  1. Oard, D.W.; He, D.; Wang, J.: User-assisted query translation for interactive cross-language information retrieval (2008) 0.00 
    0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 2030) [ClassicSimilarity], result of:
      0.014277775 = score(doc=2030,freq=8.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = 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)
  0.25 = coord(1/4)
    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. He, D.; Wu, D.: Enhancing query translation with relevance feedback in translingual information retrieval : a study of the medication process (2011) 0.00 
    0.0025760243 = product of:
  0.010304097 = sum of:
    0.010304097 = weight(_text_:information in 4244) [ClassicSimilarity], result of:
      0.010304097 = score(doc=4244,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.16796975 = fieldWeight in 4244, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4244)
  0.25 = coord(1/4)
    Abstract
    As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and translingual information retrieval (TLIR). The studies of RF in TLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in TLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolving some out-of-vocabulary terms. In this paper, we propose a novel relevance feedback method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra available translation alternatives so that the translated queries are more tuned to the current search. We studied TE using pseudo-relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve TLIR with both types of relevance feedback methods, and that the improvement is comparable to that of query expansion. More importantly, the effects of translation enhancement and query expansion are complementary. Their integration can produce further improvement, and makes TLIR more robust for a variety of queries.
    Source
    Information processing and management. 47(2011) no.1, S.1-17

Authors

Years