Search (354 results, page 1 of 18)

  • × theme_ss:"Retrievalalgorithmen"
  1. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.05
    0.0453013 = product of:
      0.0906026 = sum of:
        0.0906026 = sum of:
          0.008862185 = weight(_text_:a in 402) [ClassicSimilarity], result of:
            0.008862185 = score(doc=402,freq=2.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.20383182 = fieldWeight in 402, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.125 = fieldNorm(doc=402)
          0.08174042 = weight(_text_:22 in 402) [ClassicSimilarity], result of:
            0.08174042 = score(doc=402,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.61904186 = fieldWeight in 402, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.125 = fieldNorm(doc=402)
      0.5 = coord(1/2)
    
    Source
    Information processing and management. 22(1986) no.6, S.465-476
    Type
    a
  2. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.04
    0.04124463 = product of:
      0.08248926 = sum of:
        0.08248926 = sum of:
          0.010966395 = weight(_text_:a in 2134) [ClassicSimilarity], result of:
            0.010966395 = score(doc=2134,freq=4.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.25222903 = fieldWeight in 2134, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.109375 = fieldNorm(doc=2134)
          0.07152286 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
            0.07152286 = score(doc=2134,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.5416616 = fieldWeight in 2134, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.109375 = fieldNorm(doc=2134)
      0.5 = coord(1/2)
    
    Date
    30. 3.2001 13:32:22
    Type
    a
  3. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.04
    0.03963864 = product of:
      0.07927728 = sum of:
        0.07927728 = sum of:
          0.007754412 = weight(_text_:a in 3445) [ClassicSimilarity], result of:
            0.007754412 = score(doc=3445,freq=2.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.17835285 = fieldWeight in 3445, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.109375 = fieldNorm(doc=3445)
          0.07152286 = weight(_text_:22 in 3445) [ClassicSimilarity], result of:
            0.07152286 = score(doc=3445,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.5416616 = fieldWeight in 3445, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.109375 = fieldNorm(doc=3445)
      0.5 = coord(1/2)
    
    Date
    25. 8.2005 17:42:22
    Type
    a
  4. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.03
    0.033975974 = product of:
      0.06795195 = sum of:
        0.06795195 = sum of:
          0.006646639 = weight(_text_:a in 58) [ClassicSimilarity], result of:
            0.006646639 = score(doc=58,freq=2.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.15287387 = fieldWeight in 58, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.09375 = fieldNorm(doc=58)
          0.06130531 = weight(_text_:22 in 58) [ClassicSimilarity], result of:
            0.06130531 = score(doc=58,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.46428138 = fieldWeight in 58, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.09375 = fieldNorm(doc=58)
      0.5 = coord(1/2)
    
    Date
    14. 6.2015 22:12:44
    Type
    a
  5. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.03
    0.033975974 = product of:
      0.06795195 = sum of:
        0.06795195 = sum of:
          0.006646639 = weight(_text_:a in 2051) [ClassicSimilarity], result of:
            0.006646639 = score(doc=2051,freq=2.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.15287387 = fieldWeight in 2051, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.09375 = fieldNorm(doc=2051)
          0.06130531 = weight(_text_:22 in 2051) [ClassicSimilarity], result of:
            0.06130531 = score(doc=2051,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.46428138 = fieldWeight in 2051, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.09375 = fieldNorm(doc=2051)
      0.5 = coord(1/2)
    
    Date
    14. 6.2015 22:12:56
    Type
    a
  6. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.03
    0.025389217 = product of:
      0.050778434 = sum of:
        0.050778434 = sum of:
          0.009908224 = weight(_text_:a in 1422) [ClassicSimilarity], result of:
            0.009908224 = score(doc=1422,freq=10.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.22789092 = fieldWeight in 1422, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0625 = fieldNorm(doc=1422)
          0.04087021 = weight(_text_:22 in 1422) [ClassicSimilarity], result of:
            0.04087021 = score(doc=1422,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.30952093 = fieldWeight in 1422, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0625 = fieldNorm(doc=1422)
      0.5 = coord(1/2)
    
    Abstract
    We propose a novel approach to incorporate term similarity and inverse document frequency into a logical model of information retrieval. The ability of the logic to handle expressive representations along with the use of such classical notions are promising characteristics for IR systems. The approach proposed here has been efficiently implemented and experiments against test collections are presented.
    Date
    22. 3.2003 19:27:23
    Type
    a
  7. Faloutsos, C.: Signature files (1992) 0.02
    0.024866197 = product of:
      0.049732395 = sum of:
        0.049732395 = sum of:
          0.008862185 = weight(_text_:a in 3499) [ClassicSimilarity], result of:
            0.008862185 = score(doc=3499,freq=8.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.20383182 = fieldWeight in 3499, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0625 = fieldNorm(doc=3499)
          0.04087021 = weight(_text_:22 in 3499) [ClassicSimilarity], result of:
            0.04087021 = score(doc=3499,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.30952093 = fieldWeight in 3499, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0625 = fieldNorm(doc=3499)
      0.5 = coord(1/2)
    
    Abstract
    Presents a survey and discussion on signature-based text retrieval methods. It describes the main idea behind the signature approach and its advantages over other text retrieval methods, it provides a classification of the signature methods that have appeared in the literature, it describes the main representatives of each class, together with the relative advantages and drawbacks, and it gives a list of applications as well as commercial or university prototypes that use the signature approach
    Date
    7. 5.1999 15:22:48
    Type
    a
  8. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.02
    0.024866197 = product of:
      0.049732395 = sum of:
        0.049732395 = sum of:
          0.008862185 = weight(_text_:a in 1431) [ClassicSimilarity], result of:
            0.008862185 = score(doc=1431,freq=8.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.20383182 = fieldWeight in 1431, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0625 = fieldNorm(doc=1431)
          0.04087021 = weight(_text_:22 in 1431) [ClassicSimilarity], result of:
            0.04087021 = score(doc=1431,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.30952093 = fieldWeight in 1431, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0625 = fieldNorm(doc=1431)
      0.5 = coord(1/2)
    
    Abstract
    Properties of a percentile-based rating scale needed in bibliometrics are formulated. Based on these properties, P100 was recently introduced as a new citation-rank approach (Bornmann, Leydesdorff, & Wang, 2013). In this paper, we conceptualize P100 and propose an improvement which we call P100'. Advantages and disadvantages of citation-rank indicators are noted.
    Date
    22. 8.2014 17:05:18
    Type
    a
  9. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.02
    0.02356836 = product of:
      0.04713672 = sum of:
        0.04713672 = sum of:
          0.0062665115 = weight(_text_:a in 5108) [ClassicSimilarity], result of:
            0.0062665115 = score(doc=5108,freq=4.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.14413087 = fieldWeight in 5108, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0625 = fieldNorm(doc=5108)
          0.04087021 = weight(_text_:22 in 5108) [ClassicSimilarity], result of:
            0.04087021 = score(doc=5108,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.30952093 = fieldWeight in 5108, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0625 = fieldNorm(doc=5108)
      0.5 = coord(1/2)
    
    Date
    20. 1.2007 18:30:22
    Type
    a
  10. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.02
    0.021238474 = product of:
      0.04247695 = sum of:
        0.04247695 = sum of:
          0.006715518 = weight(_text_:a in 1319) [ClassicSimilarity], result of:
            0.006715518 = score(doc=1319,freq=6.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.1544581 = fieldWeight in 1319, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1319)
          0.03576143 = weight(_text_:22 in 1319) [ClassicSimilarity], result of:
            0.03576143 = score(doc=1319,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.2708308 = fieldWeight in 1319, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1319)
      0.5 = coord(1/2)
    
    Abstract
    Keyword based querying has been an immediate and efficient way to specify and retrieve related information that the user inquired. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given. Proposes an idea to integrate 2 existing techniques, query expansion and relevance feedback to achieve a concept-based information search for the Web
    Date
    1. 8.1996 22:08:06
    Footnote
    Contribution to a special issue devoted to the Proceedings of the 7th International World Wide Web Conference, held 14-18 April 1998, Brisbane, Australia
    Type
    a
  11. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.02
    0.02115857 = product of:
      0.04231714 = sum of:
        0.04231714 = sum of:
          0.0061926404 = weight(_text_:a in 2591) [ClassicSimilarity], result of:
            0.0061926404 = score(doc=2591,freq=10.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.14243183 = fieldWeight in 2591, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2591)
          0.0361245 = weight(_text_:22 in 2591) [ClassicSimilarity], result of:
            0.0361245 = score(doc=2591,freq=4.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.27358043 = fieldWeight in 2591, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2591)
      0.5 = coord(1/2)
    
    Abstract
    Purpose In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues. Design/methodology/approach This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods. Findings The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments. Originality/value Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.
    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
    Type
    a
  12. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.02
    0.01981932 = product of:
      0.03963864 = sum of:
        0.03963864 = sum of:
          0.003877206 = weight(_text_:a in 3276) [ClassicSimilarity], result of:
            0.003877206 = score(doc=3276,freq=2.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.089176424 = fieldWeight in 3276, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=3276)
          0.03576143 = weight(_text_:22 in 3276) [ClassicSimilarity], result of:
            0.03576143 = score(doc=3276,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.2708308 = fieldWeight in 3276, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0546875 = fieldNorm(doc=3276)
      0.5 = coord(1/2)
    
    Date
    20. 3.2005 16:23:22
    Type
    a
  13. Witschel, H.F.: Global term weights in distributed environments (2008) 0.02
    0.019722667 = product of:
      0.039445333 = sum of:
        0.039445333 = sum of:
          0.008792677 = weight(_text_:a in 2096) [ClassicSimilarity], result of:
            0.008792677 = score(doc=2096,freq=14.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.20223314 = fieldWeight in 2096, product of:
                3.7416575 = tf(freq=14.0), with freq of:
                  14.0 = termFreq=14.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=2096)
          0.030652655 = weight(_text_:22 in 2096) [ClassicSimilarity], result of:
            0.030652655 = score(doc=2096,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.23214069 = fieldWeight in 2096, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=2096)
      0.5 = coord(1/2)
    
    Abstract
    This paper examines the estimation of global term weights (such as IDF) in information retrieval scenarios where a global view on the collection is not available. In particular, the two options of either sampling documents or of using a reference corpus independent of the target retrieval collection are compared using standard IR test collections. In addition, the possibility of pruning term lists based on frequency is evaluated. The results show that very good retrieval performance can be reached when just the most frequent terms of a collection - an "extended stop word list" - are known and all terms which are not in that list are treated equally. However, the list cannot always be fully estimated from a general-purpose reference corpus, but some "domain-specific stop words" need to be added. A good solution for achieving this is to mix estimates from small samples of the target retrieval collection with ones derived from a reference corpus.
    Date
    1. 8.2008 9:44:22
    Type
    a
  14. Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996) 0.02
    0.019041913 = product of:
      0.038083825 = sum of:
        0.038083825 = sum of:
          0.0074311686 = weight(_text_:a in 6973) [ClassicSimilarity], result of:
            0.0074311686 = score(doc=6973,freq=10.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.1709182 = fieldWeight in 6973, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=6973)
          0.030652655 = weight(_text_:22 in 6973) [ClassicSimilarity], result of:
            0.030652655 = score(doc=6973,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.23214069 = fieldWeight in 6973, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=6973)
      0.5 = coord(1/2)
    
    Abstract
    Proposes that signature files be used as a viable alternative to other indexing strategies such as inverted files for searching through large volumes of text. Demonstrates through simulation, that search times can be further reduced by enhancing the basic signature file concept using deterministic partitioning algorithms which eliminate the need for an exhaustive search of the entire signature file. Reports research to evaluate the performance of some deterministic partitioning algorithms in a non simulated environment using 276 MB of raw newspaper text (taken from the Wall Street Journal) and real user queries. Presents a selection of results to illustrate trends and highlight important aspects of the performance of these methods under realistic rather than simulated operating conditions. As a result of the research reported here certain aspects of this approach to signature files are shown to be found wanting and require improvement. Suggests lines of future research on the partitioning of signature files
    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
    Type
    a
  15. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.02
    0.019041913 = product of:
      0.038083825 = sum of:
        0.038083825 = sum of:
          0.0074311686 = weight(_text_:a in 2419) [ClassicSimilarity], result of:
            0.0074311686 = score(doc=2419,freq=10.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.1709182 = fieldWeight in 2419, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=2419)
          0.030652655 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
            0.030652655 = score(doc=2419,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.23214069 = fieldWeight in 2419, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=2419)
      0.5 = coord(1/2)
    
    Abstract
    The digital library system Daffodil is targeted at strategic support of users during the information search process. For searching, exploring and managing digital library objects it provides user-customisable information seeking patterns over a federation of heterogeneous digital libraries. In this paper evaluation results with respect to retrieval effectiveness, efficiency and user satisfaction are presented. The analysis focuses on strategic support for the scientific work-flow. Daffodil supports the whole work-flow, from data source selection over information seeking to the representation, organisation and reuse of information. By embedding high level search functionality into the scientific work-flow, the user experiences better strategic system support due to a more systematic work process. These ideas have been implemented in Daffodil followed by a qualitative evaluation. The evaluation has been conducted with 28 participants, ranging from information seeking novices to experts. The results are promising, as they support the chosen model.
    Date
    16.11.2008 16:22:48
    Type
    a
  16. Campos, L.M. de; Fernández-Luna, J.M.; Huete, J.F.: Implementing relevance feedback in the Bayesian network retrieval model (2003) 0.02
    0.019041913 = product of:
      0.038083825 = sum of:
        0.038083825 = sum of:
          0.0074311686 = weight(_text_:a in 825) [ClassicSimilarity], result of:
            0.0074311686 = score(doc=825,freq=10.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.1709182 = fieldWeight in 825, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=825)
          0.030652655 = weight(_text_:22 in 825) [ClassicSimilarity], result of:
            0.030652655 = score(doc=825,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.23214069 = fieldWeight in 825, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=825)
      0.5 = coord(1/2)
    
    Abstract
    Relevance Feedback consists in automatically formulating a new query according to the relevance judgments provided by the user after evaluating a set of retrieved documents. In this article, we introduce several relevance feedback methods for the Bayesian Network Retrieval ModeL The theoretical frame an which our methods are based uses the concept of partial evidences, which summarize the new pieces of information gathered after evaluating the results obtained by the original query. These partial evidences are inserted into the underlying Bayesian network and a new inference process (probabilities propagation) is run to compute the posterior relevance probabilities of the documents in the collection given the new query. The quality of the proposed methods is tested using a preliminary experimentation with different standard document collections.
    Date
    22. 3.2003 19:30:19
    Type
    a
  17. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.02
    0.018649647 = product of:
      0.037299294 = sum of:
        0.037299294 = sum of:
          0.006646639 = weight(_text_:a in 2239) [ClassicSimilarity], result of:
            0.006646639 = score(doc=2239,freq=8.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.15287387 = fieldWeight in 2239, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=2239)
          0.030652655 = weight(_text_:22 in 2239) [ClassicSimilarity], result of:
            0.030652655 = score(doc=2239,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.23214069 = fieldWeight in 2239, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=2239)
      0.5 = coord(1/2)
    
    Abstract
    Genetic-based evolutionary learning algorithms, such as genetic algorithms (GAs) and genetic programming (GP), have been applied to information retrieval (IR) since the 1980s. Recently, GP has been applied to a new IR taskdiscovery of ranking functions for Web search-and has achieved very promising results. However, in our prior research, only one fitness function has been used for GP-based learning. It is unclear how other fitness functions may affect ranking function discovery for Web search, especially since it is weIl known that choosing a proper fitness function is very important for the effectiveness and efficiency of evolutionary algorithms. In this article, we report our experience in contrasting different fitness function designs an GP-based learning using a very large Web corpus. Our results indicate that the design of fitness functions is instrumental in performance improvement. We also give recommendations an the design of fitness functions for genetic-based information retrieval experiments.
    Date
    31. 5.2004 19:22:06
    Type
    a
  18. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.02
    0.018649647 = product of:
      0.037299294 = sum of:
        0.037299294 = sum of:
          0.006646639 = weight(_text_:a in 2717) [ClassicSimilarity], result of:
            0.006646639 = score(doc=2717,freq=8.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.15287387 = fieldWeight in 2717, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=2717)
          0.030652655 = weight(_text_:22 in 2717) [ClassicSimilarity], result of:
            0.030652655 = score(doc=2717,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.23214069 = fieldWeight in 2717, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=2717)
      0.5 = coord(1/2)
    
    Abstract
    Previous work an search-engine design has indicated that information-seekers may benefit from being given the opportunity to exploit multiple sources of evidence of document relatedness. Few existing systems, however, give users more than minimal control over the selections that may be made among methods of exploitation. By applying the methods of "document network analysis" (DNA), a unifying, graph-theoretic model of content-, collaboration-, and context-based systems (CCC) may be developed in which the nature of the similarities between types of document relatedness and document ranking are clarified. The usefulness of the approach to system design suggested by this model may be tested by constructing and evaluating a prototype system (UCXtra) that allows searchers to maintain control over the multiple ways in which document collections may be ranked and re-ranked.
    Date
    11. 9.2004 17:32:22
    Type
    a
  19. Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996) 0.02
    0.01767627 = product of:
      0.03535254 = sum of:
        0.03535254 = sum of:
          0.0046998835 = weight(_text_:a in 5123) [ClassicSimilarity], result of:
            0.0046998835 = score(doc=5123,freq=4.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.10809815 = fieldWeight in 5123, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=5123)
          0.030652655 = weight(_text_:22 in 5123) [ClassicSimilarity], result of:
            0.030652655 = score(doc=5123,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.23214069 = fieldWeight in 5123, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=5123)
      0.5 = coord(1/2)
    
    Abstract
    Traces the development of text searching and retrieval software designed to cope with the increasing demands made by the storage and handling of large amounts of data, recorded on high data storage media, from CD-ROM to multi gigabyte storage media and online information services, with particular reference to the need to cope with graphics as well as conventional ASCII text. Includes details of: Boolean searching, fuzzy searching and matching; relevance ranking; proximity searching and improved strategies for dealing with text searching in very large databases. Concludes that the best searching tools for CD-ROM publishers are those optimized for searching and retrieval on CD-ROM. CD-ROM drives have relatively lower random seek times than hard discs and so the software most appropriate to the medium is that which can effectively arrange the indexes and text on the CD-ROM to avoid continuous random access searching. Lists and reviews a selection of software packages designed to achieve the sort of results required for rapid CD-ROM searching
    Date
    12. 9.1996 13:56:22
    Type
    a
  20. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.02
    0.01767627 = product of:
      0.03535254 = sum of:
        0.03535254 = sum of:
          0.0046998835 = weight(_text_:a in 1451) [ClassicSimilarity], result of:
            0.0046998835 = score(doc=1451,freq=4.0), product of:
              0.043477926 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.037706986 = queryNorm
              0.10809815 = fieldWeight in 1451, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=1451)
          0.030652655 = weight(_text_:22 in 1451) [ClassicSimilarity], result of:
            0.030652655 = score(doc=1451,freq=2.0), product of:
              0.13204344 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.037706986 = queryNorm
              0.23214069 = fieldWeight in 1451, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=1451)
      0.5 = coord(1/2)
    
    Abstract
    Research an the use of mathematical, logical, and formal methods, has been central to Information Retrieval research for a long time. Research in this area is important not only because it helps enhancing retrieval effectiveness, but also because it helps clarifying the underlying concepts of Information Retrieval. In this article we outline some of the major aspects of the subject, and summarize the papers of this special issue with respect to how they relate to these aspects. We conclude by highlighting some directions of future research, which are needed to better understand the formal characteristics of Information Retrieval.
    Date
    22. 3.2003 19:27:36
    Type
    a

Years

Languages

Types

  • a 337
  • el 8
  • m 7
  • s 3
  • p 2
  • r 2
  • More… Less…