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Sparck Jones, K.: Automatic classification (1976)
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- Classification in the 1970s: a second look. Rev. ed. Ed.: A. Maltby
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Sparck Jones, K.: ¬A statistical interpretation of term specifity and its application in retrieval (1972)
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Sparck Jones, K.; Walker, S.; Robertson, S.E.: ¬A probabilistic model of information retrieval : development and comparative experiments - part 1 (2000)
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Sparck Jones, K.; Walker, S.; Robertson, S.E.: ¬A probabilistic model of information retrieval : development and comparative experiments - part 2 (2000)
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Sparck Jones, K.; Tait, J.I.: Automatic search term variant generation (1984)
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Sparck Jones, K.: ¬The role of artificial intelligence in information retrieval (1991)
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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
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Sparck Jones, K.; Jackson, D.M.: ¬The use of automatically obtained keyword classification for information retrieval (1970)
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Sparck Jones, K.: Index term weighting (1973)
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Robertson, S.E.; Sparck Jones, K.: Relevance weighting of search terms (1976)
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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
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Sparck Jones, K.: Retrieval system tests 1958-1978 (1981)
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Sparck Jones, K.: ¬The Cranfield tests (1981)
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Kay, M.; Sparck Jones, K.: Automated language processing (1971)
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Sparck Jones, K.: Some thoughts on classification for retrieval (1970)
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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
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Sparck Jones, K.: Some thoughts on classification for retrieval (2005)
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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.
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Sparck Jones, K.; Jones, G.J.F.; Foote, J.T.; Young, S.J.: Experiments in spoken document retrieval (1996)
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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
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Sparck Jones, K.: Revisiting classification for retrieval (2005)
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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.
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Sparck Jones, K.; Galliers, J.R.: Evaluating natural language processing systems : an analysis and review (1996)
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- Abstract
- This comprehensive state-of-the-art book is the first devoted to the important and timely issue of evaluating NLP systems. It addresses the whole area of NLP system evaluation, including aims and scope, problems and methodology. The authors provide a wide-ranging and careful analysis of evaluation concepts, reinforced with extensive illustrations; they relate systems to their environments and develop a framework for proper evaluation. The discussion of principles is completed by a detailed review of practice and strategies in the field, covering both systems for specific tasks, like translation, and core language processors. The methodology lessons drawn from the analysis and review are applied in a series of example cases. A comprehensive bibliography, a subject index, and term glossary are included
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Sparck Jones, K.: IDF term weighting and IR research lessons (2004)
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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.
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Lewis, D.D.; Sparck Jones, K.: Natural language processing for information retrieval (1996)
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Lewis, D.D.; Sparck Jones, K.: Natural language processing for information retrieval (1997)
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- 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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