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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

Sparck Jones, K.: Automatic keyword classification for information retrieval (1971) 0.01

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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.

Sparck Jones, K.: Index term weighting (1973) 0.01

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Source: Information storage and retrieval. 9(1973), S.619-633

Kay, M.; Sparck Jones, K.: Automated language processing (1971) 0.01

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Source: Annual review of information science and technology. 6(1971), S.141-166

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

Sparck Jones, K.; Rijsbergen, C.J. van: Progress in documentation : Information retrieval test collection (1976) 0.00

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Sparck Jones, K.: Some thoughts on classification for retrieval (1970) 0.00
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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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