Search (11 results, page 1 of 1)

0.0031642143 = product of:
  0.0063284286 = sum of:
    0.0063284286 = product of:
      0.012656857 = sum of:
        0.012656857 = weight(_text_:a in 1928) [ClassicSimilarity], result of:
          0.012656857 = score(doc=1928,freq=28.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.23833402 = fieldWeight in 1928, product of:
              5.2915025 = tf(freq=28.0), with freq of:
                28.0 = termFreq=28.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1928)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called 'Probabilistic indexing' allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the 'relevance number') for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing ('see' and 'see also') is based soley on the 'semantic closeness' between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can eleborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggest an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user

Type

a

0.00270615 = product of:
  0.0054123 = sum of:
    0.0054123 = product of:
      0.0108246 = sum of:
        0.0108246 = weight(_text_:a in 5465) [ClassicSimilarity], result of:
          0.0108246 = score(doc=5465,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.20383182 = fieldWeight in 5465, 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=5465)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0026473717 = product of:
  0.0052947435 = sum of:
    0.0052947435 = product of:
      0.010589487 = sum of:
        0.010589487 = weight(_text_:a in 7405) [ClassicSimilarity], result of:
          0.010589487 = score(doc=7405,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.19940455 = fieldWeight in 7405, 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=7405)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Considers 'about' as it is used in an information retrieval sense, e.g. when an indexer judges that a document is or is not about a given subject. An operational definition of 'about' is given in which it is interpreted in terms of search behaviour. Concludes that 'about' is not the central concept in document retrieval theory. A document retrieval system should provide a search output in which documents are ranked according to the probability that they will satisfy the user's information need rather that according to the degree that they are 'about' the topic. 'Aboutness' is related to satisfaction probability

Type

a

0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 1345) [ClassicSimilarity], result of:
          0.009567685 = score(doc=1345,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 1345, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=1345)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0023435948 = product of:
  0.0046871896 = sum of:
    0.0046871896 = product of:
      0.009374379 = sum of:
        0.009374379 = weight(_text_:a in 2046) [ClassicSimilarity], result of:
          0.009374379 = score(doc=2046,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17652355 = fieldWeight in 2046, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=2046)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In 1985, an article by Blair and Maron described a detailed evaluation of the effectiveness of an operational full text retrieval system used to support the defense of a large corporate lawsuit. The following year Salton published an article which called into question the conclusions of the 1985 study. The following article briefly reviews the initial study, replies to the objections raised by the secon article, and clarifies several confusions and misunderstandings of the 1985 study

Type

a

0.002269176 = product of:
  0.004538352 = sum of:
    0.004538352 = product of:
      0.009076704 = sum of:
        0.009076704 = weight(_text_:a in 7408) [ClassicSimilarity], result of:
          0.009076704 = score(doc=7408,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1709182 = fieldWeight in 7408, 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=7408)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Offers a personal look back at the origins and early use of associative search techniques, and also a look forward at more theoretical approaches to the document retrieval problems. The purpose is to contrast the following 2 different ways of improving system performance: appending associative search techniques to more or less standard (conventional) document retrieval systems; and designing document retrieval systems based on more fundamental and appropriate principles namely probabilistic design principles. Very recent work on probabilistic approaches to the document retrieval problem has provided a new (and rare) unification of 2 previously competing models. In light of this, argues that if we had to choose the best way to improve performance of a document retrieval system, it would be wiser to implement, test, and evaluate this new unified model, rather than to continue to use associative techniques which are coupled to conventionally designed retrieval systems

Type

a

0.002269176 = product of:
  0.004538352 = sum of:
    0.004538352 = product of:
      0.009076704 = sum of:
        0.009076704 = weight(_text_:a in 7409) [ClassicSimilarity], result of:
          0.009076704 = score(doc=7409,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1709182 = fieldWeight in 7409, 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=7409)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In order for conventionally designed commercial document retrieval systems to perform perfectly, the following 2 (logical) conditions must be satisfied for every search: there exists a document property (or combinations of properties) that belongs to those (and only those) documents that are relevant; that property (or combination of properties) can be correctly guessed by the searcher. In general, the 1st assumption is false, and the second is impossible to satisfy; hence no conventional IR system can perform at a maximum level of effectiveness. However, different design principles can lead to improved performance. Presents a view of the document retrieval problem that shows that since the relationship between document properties (whether they be humanly assigned index terms or words that occur in the running text) and relevance is at best probabilistic, one should approach the design problem using probabilistic principles. It turns out that a front end system designed to permit searchers to attach probabilistically interpreted weights to their query terms could be adapted for conventional IR systems. Such an enhancement could lead to improved performance

Type

a

0.0020506454 = product of:
  0.004101291 = sum of:
    0.004101291 = product of:
      0.008202582 = sum of:
        0.008202582 = weight(_text_:a in 1789) [ClassicSimilarity], result of:
          0.008202582 = score(doc=1789,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1544581 = fieldWeight in 1789, 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=1789)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

For many years it has been believed that in order to design optimal document retrieval systems one must assign index terms to documents at their optimal depth: therefore, it was of primary importance to answer the following question: "What is the optimal depth of indexing?" This article offers an analysis and answer to this question. We show that the issue of depth of indexing is, in fact, not a central issue in the design of effective document retrieval systems. It turns out that the answer to the question about optimal depth is a logical consequence of answers (which this article provides) to more fundamental questions about indexing and retrieval

Type

a

0.001913537 = product of:
  0.003827074 = sum of:
    0.003827074 = product of:
      0.007654148 = sum of:
        0.007654148 = weight(_text_:a in 7410) [ClassicSimilarity], result of:
          0.007654148 = score(doc=7410,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14413087 = fieldWeight in 7410, 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=7410)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Contribution to an issue devoted to the 6th Annual International Conference of the Special Interest Group on Information Retrieval of the Association for Computing Machinery (USA) held at the National Library of Medicine, Bethesda, Maryland, from 6-8 June 83. The following papers were presented in session 12 which was a panel on key issues in information retrieval: SALTON, G.: Research problems in automatic information retrieval; RIJSBERGEN, C.J. van: Information retrieval: new directions, old solutions; MARON, M.E.: Open problems in information retrieval

Type

a

0.001913537 = product of:
  0.003827074 = sum of:
    0.003827074 = product of:
      0.007654148 = sum of:
        0.007654148 = weight(_text_:a in 2047) [ClassicSimilarity], result of:
          0.007654148 = score(doc=2047,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14413087 = fieldWeight in 2047, 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=2047)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The motivation behind "Probabilistic Indexing" was to replace two-valued thinking about information retrieval with probabilistic notions. This involved a new view of the information retrieval problem - viewing it as problem of inference and prediction, and introducing probabilistically weighted indexes and probabilistically ranked output. These ideas were first formulated and written up in August 1958.

Type

a

0.001353075 = product of:
  0.00270615 = sum of:
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = weight(_text_:a in 7407) [ClassicSimilarity], result of:
          0.0054123 = score(doc=7407,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.10191591 = fieldWeight in 7407, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=7407)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a