Search (9 results, page 1 of 1)

  • × author_ss:"Robertson, S.E."
  • × theme_ss:"Retrievalstudien"
  1. Robertson, S.E.: ¬The parametric description of retrieval tests : Part I: The basic parameters (1969) 0.00
    0.0029000505 = product of:
      0.005800101 = sum of:
        0.005800101 = product of:
          0.011600202 = sum of:
            0.011600202 = weight(_text_:a in 4155) [ClassicSimilarity], result of:
              0.011600202 = score(doc=4155,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.21843673 = fieldWeight in 4155, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4155)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Some parameters and techniques in use for describing the results of test on IR system are analysed. Several considerations outside the scope of the usual 2X2 table are relevant to the choice of parameters. In particular, a variable which produces a 'performance curve' of a system corresponds to an extension of the 2x2 table. Also, the statistical relationships between parameters are all-important. It is considered that precision is not such a useful measure of performance (in conjunction with recall)as fallout. A more powerful alternative to Cleverdon's 'invitable inverse relationship between recall and precision'is proposed and justified, namely that the recall-fallout graph is convex.
    Type
    a
  2. Robertson, S.E.; Walker, S.; Beaulieu, M.: Experimentation as a way of life : Okapi at TREC (2000) 0.00
    0.0028703054 = product of:
      0.005740611 = sum of:
        0.005740611 = product of:
          0.011481222 = sum of:
            0.011481222 = weight(_text_:a in 6030) [ClassicSimilarity], result of:
              0.011481222 = score(doc=6030,freq=4.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.2161963 = fieldWeight in 6030, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.09375 = fieldNorm(doc=6030)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  3. Robertson, S.E.: ¬The methodology of information retrieval experiment (1981) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 3146) [ClassicSimilarity], result of:
              0.0108246 = score(doc=3146,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 3146, 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=3146)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  4. Robertson, S.E.: ¬The parametric description of retrieval tests : Part II: Overall measures (1969) 0.00
    0.0026473717 = product of:
      0.0052947435 = sum of:
        0.0052947435 = product of:
          0.010589487 = sum of:
            0.010589487 = weight(_text_:a in 4156) [ClassicSimilarity], result of:
              0.010589487 = score(doc=4156,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.19940455 = fieldWeight in 4156, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4156)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Two general requirements for overall measures of retrieval effectiveness are proposed, namely that the measures should be as far as possible independent of generality (this is interpreted to mean that it can be described in terms of recall and fallout), and that it should be able to measure the effectiveness of a performance curve (it should not be restricted to a simple 2X2 table). Several measures that have been proposed are examined with these conditions in mind. It turns out that most of the satisfactory ones are directly or indirectly related to swet's measure A, the area under the recall-fallout curve. In particular, Brookes' measure S and Rocchio's normalized recall are versions of A.
    Type
    a
  5. Robertson, S.E.; Thompson, C.L.: ¬An operational evaluation of weighting, ranking and relevance feedback via a front-end system (1987) 0.00
    0.0023678814 = product of:
      0.0047357627 = sum of:
        0.0047357627 = product of:
          0.009471525 = sum of:
            0.009471525 = weight(_text_:a in 3858) [ClassicSimilarity], result of:
              0.009471525 = score(doc=3858,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17835285 = fieldWeight in 3858, 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=3858)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
  6. Robertson, S.E.; Walker, S.; Beaulieu, M.: Laboratory experiments with Okapi : participation in the TREC programme (1997) 0.00
    0.0023678814 = product of:
      0.0047357627 = sum of:
        0.0047357627 = product of:
          0.009471525 = sum of:
            0.009471525 = weight(_text_:a in 2216) [ClassicSimilarity], result of:
              0.009471525 = score(doc=2216,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17835285 = fieldWeight in 2216, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=2216)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Briefly reviews the history of laboratory testing of information retrieval systems, focusing on the idea of a general purpose test collection of documents, queries and relevance judgements. Gives an overview of the methods used in TREC (Text Retrieval Conference) which is concerned with an ideal test collection, and discusses the Okapi team's participation in TREC. Also discusses some of the issues surrounding the difficult problem of interactive evaluation in TREC. The reconciliation of the requirements of the laboratory context with the concerns of interactive retrieval has a long way to go
    Footnote
    Contribution to a thematic issue on Okapi and information retrieval research
    Type
    a
  7. Beaulieu, M.M.; Gatford, M.; Huang, X.; Robertson, S.E.; Walker, S.; Williams, P.: Okapi an TREC-5 (1997) 0.00
    0.0020296127 = product of:
      0.0040592253 = sum of:
        0.0040592253 = product of:
          0.008118451 = sum of:
            0.008118451 = weight(_text_:a in 3097) [ClassicSimilarity], result of:
              0.008118451 = score(doc=3097,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.15287387 = fieldWeight in 3097, 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=3097)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  8. Robertson, S.E.; Sparck Jones, K.: Simple, proven approaches to text retrieval (1997) 0.00
    0.0018909799 = product of:
      0.0037819599 = sum of:
        0.0037819599 = product of:
          0.0075639198 = sum of:
            0.0075639198 = weight(_text_:a in 4532) [ClassicSimilarity], result of:
              0.0075639198 = score(doc=4532,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.14243183 = fieldWeight in 4532, 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=4532)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This technical note describes straightforward techniques for document indexing and retrieval that have been solidly established through extensive testing and are easy to apply. They are useful for many different types of text material, are viable for very large files, and have the advantage that they do not require special skills or training for searching, but are easy for end users. The document and text retrieval methods described here have a sound theoretical basis, are well established by extensive testing, and the ideas involved are now implemented in some commercial retrieval systems. Testing in the last few years has, in particular, shown that the methods presented here work very well with full texts, not only title and abstracts, and with large files of texts containing three quarters of a million documents. These tests, the TREC Tests (see Harman 1993 - 1997; IP&M 1995), have been rigorous comparative evaluations involving many different approaches to information retrieval. These techniques depend an the use of simple terms for indexing both request and document texts; an term weighting exploiting statistical information about term occurrences; an scoring for request-document matching, using these weights, to obtain a ranked search output; and an relevance feedback to modify request weights or term sets in iterative searching. The normal implementation is via an inverted file organisation using a term list with linked document identifiers, plus counting data, and pointers to the actual texts. The user's request can be a word list, phrases, sentences or extended text.
  9. Robertson, S.E.; Walker, S.; Hancock-Beaulieu, M.M.: Large test collection experiments of an operational, interactive system : OKAPI at TREC (1995) 0.00
    0.001674345 = product of:
      0.00334869 = sum of:
        0.00334869 = product of:
          0.00669738 = sum of:
            0.00669738 = weight(_text_:a in 6964) [ClassicSimilarity], result of:
              0.00669738 = score(doc=6964,freq=4.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.12611452 = fieldWeight in 6964, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=6964)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The Okapi system has been used in a series of experiments on the TREC collections, investiganting probabilistic methods, relevance feedback, and query expansion, and interaction issues. Some new probabilistic models have been developed, resulting in simple weigthing functions that take account of document length and within document and within query term frequency. All have been shown to be beneficial when based on large quantities of relevance data as in the routing task. Interaction issues are much more difficult to evaluate in the TREC framework, and no benefits have yet been demonstrated from feedback based on small numbers of 'relevant' items identified by intermediary searchers
    Type
    a