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A Hybrid Approach to Assignment of Library of Congress Subject Headings

Wartena, Christian; Franke-Maier, Michael

Abstract:
Library of Congress Subject Headings (LCSH) are popular for indexing library records. We studied the possibility of assigning LCSH automatically by training classifiers for terms used frequently in a large collection of abstracts of the literature on hand and by extracting headings from those abstracts. The resulting classifiers reach an acceptable level of precision, but fail in terms of recall partly because we could only train classifiers for a small number of LCSH. Extraction, i.e., the matching of headings in the text, produces better recall but extremely low precision. We found that combining both methods leads to a significant improvement of recall and a slight improvement of F1 score with only a small decrease in precision.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000085951/22
Veröffentlicht am 17.01.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000105121
Erschienen in Archives of Data Science, Series A (Online First)
Band 4
Heft 1
Seiten A22, 13 S. online
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