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In this article, we describe and test a two‐stage algorithm based on a lexical collocation technique which maps from the lexical clues contained in a document representation into a controlled vocabulary list of subject headings. Using a collection of 4,626 INSPEC documents, we create a “dictionary” of associations between the lexical items contained in the titles, authors, and abstracts, and controlled vocabulary subject headings assigned to those records by human indexers using a likelihood ratio statistic as the measure of association. In the deployment stage, we use the dictionary to predict which of the controlled vocabulary subject headings best describe new documents when they are presented to the system. Our evaluation of this algorithm, in which we compare the automatically assigned subject headings to the subject headings assigned to the test documents by human catalogers, shows that we can obtain results comparable to, and consistent with, human cataloging. In effect, we have cast this as a classic partial match information retrieval problem. We consider the problem to be one of “retrieving” (or assigning) the most probably “relevant” (or correct) controlled vocabulary subject headings to a document based on the clues contained in that document. © 1998 John Wiley & Sons, Inc.

There has been a massive investment in the installation of online catalogs: in selection, in the supporting infrastructure of terminals and networks, in catalog record conversion, in training, and, lately, in linking online catalogs with other online systems. In contrast, the state‐of‐the‐art of the functionality of online library catalogs has advanced little in the past few years. Rather it has been a matter of existing systems being upgraded towards the functionality of the better systems and of refinements being added. It is time for a further advance in online catalog design. We believe that the next generation of online catalogs should and will have features such as those discussed and illustrated in this article.

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