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  • × author_ss:"Bookstein, A."
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  1. Bookstein, A.: Informetric distributions : I. Unified overview (1990) 0.02
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    Date
    22. 7.2006 18:55:29
  2. Bookstein, A.: Informetric distributions : II. Resilience to ambiguity (1990) 0.02
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    Date
    22. 7.2006 18:55:55
  3. Bookstein, A.; Klein, S.T.: Compression, information theory, and grammars : a unified approach (1990) 0.02
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    Source
    ACM transaction on information systems. 8(1990), S.27-49
  4. Bookstein, A.; Cooper, W.: ¬A general mathematical model for information retrieval systems (1976) 0.02
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    Abstract
    This paper presents a mathematical model of an information retrieval system thought to be general enough to serve as an abstract representation of most document and reference retrieval systems. The model is made up of four components that, in one form or another, appear in every functioning system. It is proved that the basic organization of documents that the system provides for a user on receipt of a request follows from the properties and interrelations of the four components. Each component is then discussed in turn and it is seen that much of the existing theory regarding information systems can be viewed as an elaboration of this model
  5. Bookstein, A.: Fuzzy requests : an approach to weighted Boolean searches (1979) 0.02
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    Abstract
    This article concerns the problem of how to permit a patron tp represent the relatice importance of various index terms in a Boolean request while retaining the desirable properties of a Boolean system. The character of classical Boolean systems is reviewed and related to the notion of fuzzy sets. The fuzzy set concept then forms the basis of the concept of a fuzzy request in which weights are assigned to index terms. The properties of such a system are discussed, and it is shown that such systems retain the manipulability of traditional Boolena requests
  6. Bookstein, A.; Klein, S.T.; Raita, T.: Clumping properties of content-bearing words (1998) 0.01
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    Abstract
    Information Retrieval Systems identify content bearing words, and possibly also assign weights, as part of the process of formulating requests. For optimal retrieval efficiency, it is desirable that this be done automatically. This article defines the notion of serial clustering of words in text, and explores the value of such clustering as an indicator of a word's bearing content. This approach is flexible in the sense that it is sensitive to context: a term may be assessed as content-bearing within one collection, but not another. Our approach, being numerical, may also be of value in assigning weights to terms in requests. Experimental support is obtained from natural text databases in three different languages