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Tuscon, AZ, USA
June 7, 2004 to June 11, 2004
ISBN: 1-58113-832-6
pp: 151-159
Gary Marchionini , University of North Carolina, Chapel Hill
Miles Efron , University of North Carolina, Chapel Hill
Junliang Zhang , University of North Carolina, Chapel Hill
ABSTRACT
This paper describes ongoing research into the application of machine learning techniques for improving access to governmental information in complex digital libraries. Under the auspices of the GovStat Project, our goal is to identify a small number of semantically valid concepts that adequately spans the intellectual domain of a collection. The goal of this discovery is twofold. First we desire a practical aid for information architects. Second, automatically derived document-concept relationships are a necessary precondition for real-world deployment of many dynamic interfaces. The current study compares concept learning strategies based on three document representations: keywords, titles, and full-text. In statistical and user-based studies, human-created keywords provide significant improvements in concept learning over both title-only and full-text representations.
INDEX TERMS
Machine Learning, Information Architecture, Interface Design
CITATION
Gary Marchionini, Miles Efron, Junliang Zhang, "Machine Learning for Information Architecture in a Large Governmental Website", JCDL, 2004, Digital Libraries, Joint Conference on, Digital Libraries, Joint Conference on 2004, pp. 151-159, doi:10.1109/JCDL.2004.1336112
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