Search (36 results, page 1 of 2)

  • × author_ss:"Sparck Jones, K."
  1. Sparck Jones, K.: Search term relevance weighting given little relevance information (1979) 0.04 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.329-338.
    Source
    Journal of documentation. 35(1979), S.30-48
    Type
    a
  2. Strzalkowski, T.; Sparck Jones, K.: NLP track at TREC-5 (1997) 0.03 
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    Pages
    S.97-101
    Source
    The Fifth Text Retrieval Conference (TREC-5). Ed.: E.M. Voorhees u. D.K. Harman
    Type
    a
  3. Sparck Jones, K.: Metareflections on TREC (2005) 0.03 
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    Pages
    S.421-448
    Source
    TREC: experiment and evaluation in information retrieval. Ed.: E.M. Voorhees, u. D.K. Harman
    Type
    a
  4. Sparck Jones, K.; Kay, M.: Linguistik und Informationswissenschaft (1976) 0.03 
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    Issue
    Aus dem Englischen übersetzt von E. Couper u. R. Kuhlen.
    Pages
    261 S
  5. Sparck Jones, K.: Retrieval system tests 1958-1978 (1981) 0.03 
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    Pages
    S.213-255
    Source
    Information retrieval experiment. Ed.: K. Sparck Jones
    Type
    a
  6. Sparck Jones, K.: ¬The Cranfield tests (1981) 0.03 
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    Pages
    S.256-284
    Source
    Information retrieval experiments. Ed.: K. Sparck Jones
    Type
    a
  7. Sparck Jones, K.; Jones, G.J.F.; Foote, J.T.; Young, S.J.: Experiments in spoken document retrieval (1996) 0.02 
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    Abstract
    Describes experiments in the retrieval of spoken documents in multimedia systems. Speech documents pose a particular problem for retrieval since their words as well as contents are unknown. Addresses this problem, for a video mail application, by combining state of the art speech recognition with established document retrieval technologies so as to provide an effective and efficient retrieval tool. Tests with a small spoken message collection show that retrieval precision for the spoken file can reach 90% of that obtained when the same file is used, as a benchmark, in text transcription form
    Footnote
    Wiederabdruck in: Readings in informatio retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.493-502.
    Source
    Information processing and management. 32(1996) no.4, S.399-417
    Type
    a
  8. Sparck Jones, K.: Automatic classification (1976) 0.02 
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    Pages
    S.209-225
    Source
    Classification in the 1970s: a second look. Rev. ed. Ed.: A. Maltby
    Type
    a
  9. Sparck Jones, K.: ¬A statistical interpretation of term specifity and its application in retrieval (1972) 0.02 
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    Source
    Journal of documentation. 28(1972), S.11-21
    Type
    a
  10. Sparck Jones, K.; Tait, J.I.: Automatic search term variant generation (1984) 0.02 
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    Journal of documentation. 40(1984), S.50-66
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  11. Sparck Jones, K.; Jackson, D.M.: ¬The use of automatically obtained keyword classification for information retrieval (1970) 0.02 
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    Information storage and retrieval. 5(1970), S.175-201
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  12. Sparck Jones, K.: Index term weighting (1973) 0.02 
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    Information storage and retrieval. 9(1973), S.619-633
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  13. Kay, M.; Sparck Jones, K.: Automated language processing (1971) 0.02 
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    Annual review of information science and technology. 6(1971), S.141-166
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  14. Lewis, D.D.; Sparck Jones, K.: Natural language processing for information retrieval (1996) 0.02 
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  15. Sparck Jones, K.; Walker, S.; Robertson, S.E.: ¬A probabilistic model of information retrieval : development and comparative experiments - part 1 (2000) 0.02 
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  16. Sparck Jones, K.; Walker, S.; Robertson, S.E.: ¬A probabilistic model of information retrieval : development and comparative experiments - part 2 (2000) 0.02 
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  17. Sparck Jones, K.: IDF term weighting and IR research lessons (2004) 0.01 
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    Abstract
    Robertson comments on the theoretical status of IDF term weighting. Its history illustrates how ideas develop in a specific research context, in theory/experiment interaction, and in operational practice.
    Source
    Journal of documentation. 60(2004) no.5, S.521-523
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  18. Needham, R.M.; Sparck Jones, K.: Keywords and clumps (1985) 0.01 
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    Abstract
    The selection that follows was chosen as it represents "a very early paper an the possibilities allowed by computers an documentation." In the early 1960s computers were being used to provide simple automatic indexing systems wherein keywords were extracted from documents. The problem with such systems was that they lacked vocabulary control, thus documents related in subject matter were not always collocated in retrieval. To improve retrieval by improving recall is the raison d'être of vocabulary control tools such as classifications and thesauri. The question arose whether it was possible by automatic means to construct classes of terms, which when substituted, one for another, could be used to improve retrieval performance? One of the first theoretical approaches to this question was initiated by R. M. Needham and Karen Sparck Jones at the Cambridge Language Research Institute in England.t The question was later pursued using experimental methodologies by Sparck Jones, who, as a Senior Research Associate in the Computer Laboratory at the University of Cambridge, has devoted her life's work to research in information retrieval and automatic naturai language processing. Based an the principles of numerical taxonomy, automatic classification techniques start from the premise that two objects are similar to the degree that they share attributes in common. When these two objects are keywords, their similarity is measured in terms of the number of documents they index in common. Step 1 in automatic classification is to compute mathematically the degree to which two terms are similar. Step 2 is to group together those terms that are "most similar" to each other, forming equivalence classes of intersubstitutable terms. The technique for forming such classes varies and is the factor that characteristically distinguishes different approaches to automatic classification. The technique used by Needham and Sparck Jones, that of clumping, is described in the selection that follows. Questions that must be asked are whether the use of automatically generated classes really does improve retrieval performance and whether there is a true eco nomic advantage in substituting mechanical for manual labor. Several years after her work with clumping, Sparck Jones was to observe that while it was not wholly satisfactory in itself, it was valuable in that it stimulated research into automatic classification. To this it might be added that it was valuable in that it introduced to libraryl information science the methods of numerical taxonomy, thus stimulating us to think again about the fundamental nature and purpose of classification. In this connection it might be useful to review how automatically derived classes differ from those of manually constructed classifications: 1) the manner of their derivation is purely a posteriori, the ultimate operationalization of the principle of literary warrant; 2) the relationship between members forming such classes is essentially statistical; the members of a given class are similar to each other not because they possess the class-defining characteristic but by virtue of sharing a family resemblance; and finally, 3) automatically derived classes are not related meaningfully one to another, that is, they are not ordered in traditional hierarchical and precedence relationships.
    Footnote
    Original in: Journal of documentation 20(1964) no.1, S.5-15.
    Pages
    S.262-272
    Source
    Theory of subject analysis: a sourcebook. Ed.: L.M. Chan, et al
    Type
    a
  19. Lewis, D.D.; Sparck Jones, K.: Natural language processing for information retrieval (1997) 0.01 
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    Footnote
    Wiederabdruck aus: Communications of the ACM 39(1996) no.1, S.92-101
    Pages
    S.49-61
    Source
    From classification to 'knowledge organization': Dorking revisited or 'past is prelude'. A collection of reprints to commemorate the firty year span between the Dorking Conference (First International Study Conference on Classification Research 1957) and the Sixth International Study Conference on Classification Research (London 1997). Ed.: A. Gilchrist
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  20. Sparck Jones, K.: ¬The role of artificial intelligence in information retrieval (1991) 0.01 
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    Abstract
    Presents a view of the scope of artificial intelligence (AI) in information retrieval (IR). Considers potential roles of AI and IR, evaluating AI from a realistic point od view and within a wide information management potential, not just because AI is itself insufficiently developed, but because many information management tasks are properly shallow information processing ones. There is nevertheless an important place for specific applications of AI or AI-derived technology when particular constraints can be placed on the information management tasks involved
    Source
    Journal of the American Society for Information Science. 42(1991) no.8, S.558-565
    Type
    a

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