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  • × author_ss:"Sparck Jones, K."
  1. Sparck Jones, K.: ¬The role of artificial intelligence in information retrieval (1991) 0.02 
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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
  2. Sparck Jones, K.; Jackson, D.M.: ¬The use of automatically obtained keyword classification for information retrieval (1970) 0.01 
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    Source
    Information storage and retrieval. 5(1970), S.175-201
  3. Sparck Jones, K.: Retrieval system tests 1958-1978 (1981) 0.01 
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    Source
    Information retrieval experiment. Ed.: K. Sparck Jones
  4. Sparck Jones, K.: Automatic keyword classification for information retrieval (1971) 0.01 
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  5. Sparck Jones, K.: Index term weighting (1973) 0.01 
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    Source
    Information storage and retrieval. 9(1973), S.619-633
  6. Sparck Jones, K.: ¬The Cranfield tests (1981) 0.01 
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    Source
    Information retrieval experiments. Ed.: K. Sparck Jones
  7. Sparck Jones, K.; Jones, G.J.F.; Foote, J.T.; Young, S.J.: Experiments in spoken document retrieval (1996) 0.01 
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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
  8. Lewis, D.D.; Sparck Jones, K.: Natural language processing for information retrieval (1996) 0.01 
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  9. Sparck Jones, K.; Walker, S.; Robertson, S.E.: ¬A probabilistic model of information retrieval : development and comparative experiments - part 1 (2000) 0.01 
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    Source
    Information processing and management. 36(2000) no.6, S.779-808
  10. Sparck Jones, K.; Walker, S.; Robertson, S.E.: ¬A probabilistic model of information retrieval : development and comparative experiments - part 2 (2000) 0.01 
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    Source
    Information processing and management. 36(2000) no.6, S.809-840
  11. Sparck Jones, K.: Search term relevance weighting given little relevance information (1979) 0.01 
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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.
  12. Sparck Jones, K.: Revisiting classification for retrieval (2005) 0.01 
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    Abstract
    Purpose - This short note seeks to respond to Hjørland and Pederson's paper "A substantive theory of classification for information retrieval" which starts from Sparck Jones's, "Some thoughts on classification for retrieval", originally published in 1970. Design/methodology/approach - The note comments on the context in which the 1970 paper was written, and on Hjørland and Pedersen's views, emphasising the need for well-grounded classification theory and application. Findings - The note maintains that text-based, a posteriori, classification, as increasingly found in applications, is likely to be more useful, in general, than a priori classification. Originality/value - The note elaborates on points made in a well-received earlier paper.
    Theme
    Klassifikationssysteme im Online-Retrieval
  13. Sparck Jones, K.: Metareflections on TREC (2005) 0.01 
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    Source
    TREC: experiment and evaluation in information retrieval. Ed.: E.M. Voorhees, u. D.K. Harman
  14. Sparck Jones, K.: Some thoughts on classification for retrieval (2005) 0.01 
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    Abstract
    Purpose - This paper was originally published in 1970 (Journal of documentation. 26(1970), S.89-101), considered the suggestion that classifications for retrieval should be constructed automatically and raised some serious problems concerning the sorts of classification which were required, and the way in which formal classification theories should be exploited, given that a retrieval classification is required for a purpose. These difficulties had not been sufficiently considered, and the paper, therefore, aims to attempt an analysis of them, though no solutions of immediate application could be suggested. Design/methodology/approach - Starting with the illustrative proposition that a polythetic, multiple, unordered classification is required in automatic thesaurus construction, this is considered in the context of classification in general, where eight sorts of classification can be distinguished, each covering a range of class definitions and class-finding algorithms. Findings - Since there is generally no natural or best classification of a set of objects as such, the evaluation of alternative classifications requires either formal criteria of goodness of fit, or, if a classification is required for a purpose, a precise statement of that purpose. In any case a substantive theory of classification is needed, which does not exist; and, since sufficiently precise specifications of retrieval requirements are also lacking, the only currently available approach to automatic classification experiments for information retrieval is to do enough of them. Originality/value - Gives insights into the classification of material for information retrieval.
    Theme
    Klassifikationssysteme im Online-Retrieval
  15. Sparck Jones, K.: Reflections on TREC : TREC-2 (1995) 0.01 
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    Abstract
    Discusses the TREC programme as a major enterprise in information retrieval research. It reviews its structure as an evaluation exercise, characterises the methods of indexing and retrieval being tested within it in terms of the approaches to system performance factors these represent; analyses the test results for solid, overall conclusions that can be drawn from them; and, in the light of the particular features of the test data, assesses TREC both for generally applicable findings that emerge from it and for directions it offers for future research
    Source
    Information processing and management. 31(1995) no.3, S.291-314
  16. Sparck Jones, K.; Rijsbergen, C.J. van: Progress in documentation : Information retrieval test collection (1976) 0.01 
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    Abstract
    Many retrieval experiments have been based on inadequate test collections, and current research is hampered by the lack of proper collections. This short review does not attempt a fully docuemted survey of all the collections used in the past decade: hopefully representative examples have been studied to throw light on the requriements test collections should meet, to show how past collections have been defective, and to suggest guidelines for a future "ideal" test collection. This specifications for this collection can be taken as an indirect comment on our present state of knowledge of major retrieval system variables, and experience in conducting experiments.
  17. Sparck Jones, K.: Some thoughts on classification for retrieval (1970) 0.01 
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    Abstract
    The suggestion that classifications for retrieval should be constructed automatically raises some serious problems concerning the sorts of classification which are required, and the way in which formal classification theories should be exploited, given that a retrieval classification is required for a purpose. These difficulties have not been sufficiently considered, and the paper therefore attempts an analysis of them, though no solution of immediate application can be suggested. Starting with the illustrative proposition that a polythetic, multiple, unordered classification is required in automatic thesaurus construction, this is considered in the context of classification in general, where eight sorts of classification can be distinguished, each covering a range of class definitions and class-finding algorithms. The problem which follows is that since there is generally no natural or best classification of a set of objects as such, the evaluation of alternative classifications requires either formal criteria of goodness of fit, or, if a classification is required for a purpose, a precises statement of that purpose. In any case a substantive theory of classification is needed, which does not exist; and since sufficiently precise specifications of retrieval requirements are also lacking, the only currently available approach to automatic classification experiments for information retrieval is to do enough of them
    Theme
    Klassifikationssysteme im Online-Retrieval
  18. Sparck Jones, K.: Reflections on TREC (1997) 0.01 
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    Abstract
    This paper discusses the Text REtrieval Conferences (TREC) programme as a major enterprise in information retrieval research. It reviews its structure as an evaluation exercise, characterises the methods of indexing and retrieval being tested within its terms of the approaches to system performance factors these represent; analyses the test results for solid, overall conclusions that can be drawn from them; and, in the light of the particular features of the test data, assesses TREC both for generally applicable findings that emerge from it and for directions it offers for future research
    Footnote
    Wiederabdruck aus: Information processing and management 31(1995) no.3, S.192-314
    Imprint
    The Hague : International Federation for Information and Documentation (FID)
  19. Robertson, S.E.; Sparck Jones, K.: Simple, proven approaches to text retrieval (1997) 0.01 
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    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.
  20. Robertson, S.E.; Sparck Jones, K.: Relevance weighting of search terms (1976) 0.01 
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    Abstract
    Examines statistical techniques for exploiting relevance information to weight search terms. These techniques are presented as a natural extension of weighting methods using information about the distribution of index terms in documents in general. A series of relevance weighting functions is derived and is justified by theoretical considerations. In particular, it is shown that specific weighted search methods are implied by a general probabilistic theory of retrieval. Different applications of relevance weighting are illustrated by experimental results for test collections
    Source
    Journal of the American Society for Information Science. 27(1976), S.129-146

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