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Automatic indexing of documents from journal descriptors: A preliminary investigation

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  • Susanne M. Humphrey

Abstract

A new, fully automated approach for indexing documents is presented based on associating textwords in a training set of bibliographic citations with the indexing of journals. This journal‐level indexing is in the form of a consistent, timely set of journal descriptors (JDs) indexing the individual journals themselves. This indexing is maintained in journal records in a serials authority database. The advantage of this novel approach is that the training set does not depend on previous manual indexing of hundreds of thousands of documents (i.e., any such indexing already in the training set is not used), but rather the relatively small intellectual effort of indexing at the journal level, usually a matter of a few thousand unique journals for which retrospective indexing to maintain consistency and currency may be feasible. If successful, JD indexing would provide topical categorization of documents outside the training set, i.e., journal articles, monographs, WEB documents, reports from the grey literature, etc., and therefore be applied in searching. Because JDs are quite general, corresponding to subject domains, their most probable use would be for improving or refining search results.

Suggested Citation

  • Susanne M. Humphrey, 1999. "Automatic indexing of documents from journal descriptors: A preliminary investigation," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(8), pages 661-674.
  • Handle: RePEc:bla:jamest:v:50:y:1999:i:8:p:661-674
    DOI: 10.1002/(SICI)1097-4571(1999)50:83.0.CO;2-R
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    1. Thompson, Scott A. & Loveland, James M. & Fombelle, Paul W., 2014. "Thematic Discrepancy Analysis: A Method to Gain Insights into Lurkers and Test for Non-Response Bias," Journal of Interactive Marketing, Elsevier, vol. 28(1), pages 55-67.

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