TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Wartena, Christian A1 - Franke-Maier, Michael T1 - A Hybrid Approach to Assignment of Library of Congress Subject Headings JF - Archives of Data Science, Series A N2 - 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. KW - Classification KW - Keyword Extraction KW - LCSH KW - Machine Learning KW - Library of Congress KW - Schlagwort KW - Automatische Klassifikation KW - Maschinelles Lernen Y1 - 2018 UN - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-15658 UR - https://publikationen.bibliothek.kit.edu/1000105121 SN - 2363-9881 SS - 2363-9881 U6 - https://doi.org/10.25968/opus-1565 DO - https://doi.org/10.25968/opus-1565 VL - 4 IS - 1 ER -