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.

In this article we present an algorithm which automatically extracts geopositional coordinate index terms from text to support georeferenced document indexing and retrieval. Under this algorithm, words and phrases containing geographic place names or characteristics are extracted from a text document and used as input to database functions which use spatial reasoning to approximate statistically the geoposition being referenced in the text. We conclude with a discussion of preliminary results and future work. © 1994 John Wiley & Sons, Inc.

We argue that the advent of large volumes of full-length text, as opposed to short texts like abstracts and newswire, should be accompanied by corresponding new approaches to information access. Toward this end, we discuss the merits of imposing structure on full-length text documents; that is, a partition of the text into coherent multi-paragraph units that represent the pattern of subtopics that comprise the text. Using this structure, we can make a distinction between the main topics, which occur throughout the length of the text, and the subtopics, which are of only limited extent. We discuss why recognition of subtopic structure is important and how, to some degree of accuracy, it can be found. We describe a new way of specifying queries on full-length documents and then describe an experiment in which making use of the recognition of local structure achieves better results on a typical information retrieval task than does a standard IR measure.