Document (#43374)

Author
Sack, H.
Title
Hybride Künstliche Intelligenz in der automatisierten Inhaltserschließung
Source
Qualität in der Inhaltserschließung. Hrsg.: M. Franke-Maier, u.a
Imprint
München : DeGruyter-Saur
Year
2021
Pages
S.387-405
Series
Bibliotheks- und Informationspraxis; 70
Abstract
Effizienter (Online-)Zugang zu Bibliotheks- und Archivmaterialien erfordert eine qualitativ hinreichende inhaltliche Erschließung dieser Dokumente. Die passgenaue Verschlagwortung und Kategorisierung dieser unstrukturierten Dokumente ermöglichen einen strukturell gegliederten Zugang sowohl in der analogen als auch in der digitalen Welt. Darüber hinaus erweitert eine vollständige Transkription der Dokumente den Zugang über die Möglichkeiten der Volltextsuche. Angesichts der in jüngster Zeit erzielten spektakulären Erfolge der Künstlichen Intelligenz liegt die Schlussfolgerung nahe, dass auch das Problem der automatisierten Inhaltserschließung für Bibliotheken und Archive als mehr oder weniger gelöst anzusehen wäre. Allerdings lassen sich die oftmals nur in thematisch engen Teilbereichen erzielten Erfolge nicht immer problemlos verallgemeinern oder in einen neuen Kontext übertragen. Das Ziel der vorliegenden Darstellung liegt in der Diskussion des aktuellen Stands der Technik der automatisierten inhaltlichen Erschließung anhand ausgewählter Beispiele sowie möglicher Fortschritte und Prognosen basierend auf aktuellen Entwicklungen des maschinellen Lernens und der Künstlichen Intelligenz einschließlich deren Kritik.
Theme
Automatisches Indexieren

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