Search (6 results, page 1 of 1)

0.06281641 = product of:
  0.18844922 = sum of:
    0.18844922 = weight(_text_:query in 5446) [ClassicSimilarity], result of:
      0.18844922 = score(doc=5446,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.8215931 = fieldWeight in 5446, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.125 = fieldNorm(doc=5446)
  0.33333334 = coord(1/3)

0.06281641 = product of:
  0.18844922 = sum of:
    0.18844922 = weight(_text_:query in 6774) [ClassicSimilarity], result of:
      0.18844922 = score(doc=6774,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.8215931 = fieldWeight in 6774, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.125 = fieldNorm(doc=6774)
  0.33333334 = coord(1/3)

0.052673157 = product of:
  0.15801947 = sum of:
    0.15801947 = weight(_text_:query in 5699) [ClassicSimilarity], result of:
      0.15801947 = score(doc=5699,freq=10.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.68892676 = fieldWeight in 5699, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.046875 = fieldNorm(doc=5699)
  0.33333334 = coord(1/3)

Abstract

The Smart information retrieval project emphazises completely automatic approaches to the understanding and retrieval of large quantities of text. The work in the TREC-2 environment continues, performing both routing and ad hoc experiments. The ad hoc work extends investigations into combining global similarities, giving an overall indication of how a document matches a query, with local similarities identifying a smaller part of the document that matches the query. The performance of ad hoc runs is good, but it is clear that full advantage of the available local information is not been taken advantage of. The routing experiments use conventional relevance feedback approaches to routing, but with a much greater degree of query expansion than was previously done. The length of a query vector is increased by a factor of 5 to 10 by adding terms found in previously seen relevant documents. This approach improves effectiveness by 30-40% over the original query

0.03886567 = product of:
  0.116597004 = sum of:
    0.116597004 = weight(_text_:query in 4151) [ClassicSimilarity], result of:
      0.116597004 = score(doc=4151,freq=4.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.5083348 = fieldWeight in 4151, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4151)
  0.33333334 = coord(1/3)

Abstract

Two widerly used criteria for evaluating the effectiveness of information retrieval systems are, respectively, the recall and the precision. Since the determiniation of these measures is dependent on a distinction between documents which are relevant to a given query and documents which are not relevant to that query, it has sometimes been claimed that an accurate, generally valid evaluation cannot be based on recall and precision measure. A study was made to determine the effect of variations in relevance assesments do not produce significant variations in average recall and precision. It thus appears that properly computed recall and precision data may represent effectiveness indicators which are gemerally valid for many distinct user classes.

0.031408206 = product of:
  0.09422461 = sum of:
    0.09422461 = weight(_text_:query in 4836) [ClassicSimilarity], result of:
      0.09422461 = score(doc=4836,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.41079655 = fieldWeight in 4836, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0625 = fieldNorm(doc=4836)
  0.33333334 = coord(1/3)

Abstract

Reports on the experimental evaluation of an adaptive linear model that constructs improved user query vectors from user preference judgements on a sample set of documents. The performance of this method is compared with that of the standard relevance feedback techniques. The experimental results seem to demonstrate the effectiveness of the adaptive method

0.031408206 = product of:
  0.09422461 = sum of:
    0.09422461 = weight(_text_:query in 5442) [ClassicSimilarity], result of:
      0.09422461 = score(doc=5442,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.41079655 = fieldWeight in 5442, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0625 = fieldNorm(doc=5442)
  0.33333334 = coord(1/3)

Abstract

Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate the effectiveness of the various methods. Prescriptions are given for conducting text retrieval operations iteratively using relevance feedback