Search (8 results, page 1 of 1)

0.009356454 = product of:
  0.04366345 = sum of:
    0.017435152 = weight(_text_:web in 819) [ClassicSimilarity], result of:
      0.017435152 = score(doc=819,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.18028519 = fieldWeight in 819, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=819)
    0.0050448296 = weight(_text_:information in 819) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=819,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 819, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=819)
    0.021183468 = weight(_text_:retrieval in 819) [ClassicSimilarity], result of:
      0.021183468 = score(doc=819,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23632148 = fieldWeight in 819, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=819)
  0.21428572 = coord(3/14)

Abstract

Purpose - An issue that tends to be ignored in information retrieval is the issue of updating inverted files. This is largely because inverted files were devised to provide fast query service, and much work has been done with the emphasis strongly on queries. This paper aims to study the effect of using parallel methods for the update of inverted files in order to reduce costs, by looking at two types of partitioning for inverted files: document identifier and term identifier. Design/methodology/approach - Raw update service and update with query service are studied with these partitioning schemes using an incremental update strategy. The paper uses standard measures used in parallel computing such as speedup to examine the computing results and also the costs of reorganising indexes while servicing transactions. Findings - Empirical results show that for both transaction processing and index reorganisation the document identifier method is superior. However, there is evidence that the term identifier partitioning method could be useful in a concurrent transaction processing context. Practical implications - There is an increasing need to service updates, which is now becoming a requirement of inverted files (for dynamic collections such as the web), demonstrating that a shift in requirements of inverted file maintenance is needed from the past. Originality/value - The paper is of value to database administrators who manage large-scale and dynamic text collections, and who need to use parallel computing to implement their text retrieval services.

0.006865305 = product of:
  0.04805713 = sum of:
    0.012107591 = weight(_text_:information in 1935) [ClassicSimilarity], result of:
      0.012107591 = score(doc=1935,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.23274569 = fieldWeight in 1935, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=1935)
    0.03594954 = weight(_text_:retrieval in 1935) [ClassicSimilarity], result of:
      0.03594954 = score(doc=1935,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.40105087 = fieldWeight in 1935, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=1935)
  0.14285715 = coord(2/14)

Footnote

Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willet. San Francisco: Morgan Kaufmann 1997. S.281-286.

0.006371425 = product of:
  0.044599973 = sum of:
    0.033893548 = weight(_text_:retrieval in 5108) [ClassicSimilarity], result of:
      0.033893548 = score(doc=5108,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.37811437 = fieldWeight in 5108, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=5108)
    0.010706427 = product of:
      0.032119278 = sum of:
        0.032119278 = weight(_text_:22 in 5108) [ClassicSimilarity], result of:
          0.032119278 = score(doc=5108,freq=2.0), product of:
            0.103770934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029633347 = 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)
      0.33333334 = coord(1/3)
  0.14285715 = coord(2/14)

Abstract

In this paper methods for both speeding up passage processing and examining more passages using parallel computers are explored. The number of passages processed are varied in order to examine the effect on retrieval effectiveness and efficiency. The particular algorithm applied has previously been used to good effect in Okapi experiments at TREC. This algorithm and the mechanism for applying parallel computing to speed up processing are described.

Date

20. 1.2007 18:30:22

0.0061978353 = product of:
  0.043384846 = sum of:
    0.0070627616 = weight(_text_:information in 5694) [ClassicSimilarity], result of:
      0.0070627616 = score(doc=5694,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.13576832 = fieldWeight in 5694, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5694)
    0.036322083 = weight(_text_:retrieval in 5694) [ClassicSimilarity], result of:
      0.036322083 = score(doc=5694,freq=6.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.40520695 = fieldWeight in 5694, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5694)
  0.14285715 = coord(2/14)

Abstract

Reports text information retrieval experiments performed as part of the 3 rd round of Text Retrieval Conferences (TREC) using the Okapi online catalogue system at City University, UK. The emphasis in TREC-3 was: further refinement of term weighting functions; an investigation of run time passage determination and searching; expansion of ad hoc queries by terms extracted from the top documents retrieved by a trial search; new methods for choosing query expansion terms after relevance feedback, now split into methods of ranking terms prior to selection and subsequent selection procedures; and the development of a user interface procedure within the new TREC interactive search framework

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0058040675 = product of:
  0.04062847 = sum of:
    0.0071344664 = weight(_text_:information in 4532) [ClassicSimilarity], result of:
      0.0071344664 = score(doc=4532,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.13714671 = fieldWeight in 4532, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4532)
    0.033494003 = weight(_text_:retrieval in 4532) [ClassicSimilarity], result of:
      0.033494003 = score(doc=4532,freq=10.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.37365708 = fieldWeight in 4532, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4532)
  0.14285715 = coord(2/14)

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.

0.005721087 = product of:
  0.04004761 = sum of:
    0.010089659 = weight(_text_:information in 7953) [ClassicSimilarity], result of:
      0.010089659 = score(doc=7953,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.19395474 = fieldWeight in 7953, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=7953)
    0.029957948 = weight(_text_:retrieval in 7953) [ClassicSimilarity], result of:
      0.029957948 = score(doc=7953,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.33420905 = fieldWeight in 7953, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.078125 = fieldNorm(doc=7953)
  0.14285715 = coord(2/14)

Abstract

Describes the work carried out on the TREC-2 project following the results of the TREC-1 project. Experiments were conducted on the OKAPI experimental text information retrieval system which investigated a number of alternative probabilistic term weighting functions in place of the 'standard' Robertson Sparck Jones weighting functions used in TREC-1

0.0044962796 = product of:
  0.031473957 = sum of:
    0.0060537956 = weight(_text_:information in 651) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=651,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.116372846 = fieldWeight in 651, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=651)
    0.025420163 = weight(_text_:retrieval in 651) [ClassicSimilarity], result of:
      0.025420163 = score(doc=651,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.2835858 = fieldWeight in 651, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=651)
  0.14285715 = coord(2/14)

Abstract

Purpose - The generation of inverted indexes is one of the most computationally intensive activities for information retrieval systems: indexing large multi-gigabyte text databases can take many hours or even days to complete. We examine the generation of partitioned inverted files in order to speed up the process of indexing. Two types of index partitions are investigated: TermId and DocId. Design/methodology/approach - We use standard measures used in parallel computing such as speedup and efficiency to examine the computing results and also the space costs of our trial indexing experiments. Findings - The results from runs on both partitioning methods are compared and contrasted, concluding that DocId is the more efficient method. Practical implications - The practical implications are that the DocId partitioning method would in most circumstances be used for distributing inverted file data in a parallel computer, particularly if indexing speed is the primary consideration. Originality/value - The paper is of value to database administrators who manage large-scale text collections, and who need to use parallel computing to implement their text retrieval services.

0.001008966 = product of:
  0.014125523 = sum of:
    0.014125523 = weight(_text_:information in 788) [ClassicSimilarity], result of:
      0.014125523 = score(doc=788,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.27153665 = fieldWeight in 788, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=788)
  0.071428575 = coord(1/14)

Source

Information technology: research and development. 3(1984) no.1, S.84-87