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Source

Journal of documentation. 40(1984), S.50-66

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Source

Journal of documentation. 28(1972), S.11-21

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Source

Journal of documentation. 35(1979), S.30-48

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Source

Journal of documentation. 60(2004) no.5, S.521-523

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Source

Journal of documentation. 32(1976) no.1, S.59-75

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Imprint

The Hague : International Federation for Information and Documentation (FID)

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Footnote

Wiederabdruck in: Journal of documentation. 61(2005) no.5, S.571-581.

Source

Journal of documentation. 26(1970), S.89-101

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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.

Source

Journal of documentation. 61(2005) no.5, S.571-581

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Source

Journal of documentation. 61(2005) no.5, S.598-601

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Source

Journal of documentation. 60(2004) no.5, S.493-502

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Imprint

The Hague : International Federation for Information and Documentation (FID)

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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.