Search (5 results, page 1 of 1)

  • × theme_ss:"Automatisches Indexieren"
  • × type_ss:"a"
  • × type_ss:"el"
  • × year_i:[2020 TO 2030}
  1. Pielmeier, S.; Voß, V.; Carstensen, H.; Kahl, B.: Online-Workshop "Computerunterstützte Inhaltserschließung" 2020 (2021) 0.00 
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    Abstract
    Zum ersten Mal in digitaler Form und mit 230 Teilnehmer*innen fand am 11. und 12. November 2020 der 4. Workshop "Computerunterstützte Inhaltserschließung" statt, organisiert von der Deutschen Nationalbibliothek (DNB), der Firma Eurospider Information Technology, der Staatsbibliothek zu Berlin - Preußischer Kulturbesitz (SBB), der UB Stuttgart und dem Bibliotheksservice-Zentrum Baden-Württemberg (BSZ). Im Mittelpunkt stand der "Digitale Assistent DA-3": In elf Vorträgen wurden Anwendungsszenarien und Erfahrungen mit dem System vorgestellt, das Bibliotheken und andere Wissenschafts- und Kultureinrichtungen bei der Inhaltserschließung unterstützen soll. Die Begrüßung und Einführung in die beiden Workshop-Tage übernahm Frank Scholze (Generaldirektor der DNB). Er sieht den DA-3 als Baustein für die Verzahnung der intellektuellen und der maschinellen Erschließung.
    Type
    a
  2. Franke-Maier, M.; Beck, C.; Kasprzik, A.; Maas, J.F.; Pielmeier, S.; Wiesenmüller, H: ¬Ein Feuerwerk an Algorithmen und der Startschuss zur Bildung eines Kompetenznetzwerks für maschinelle Erschließung : Bericht zur Fachtagung Netzwerk maschinelle Erschließung an der Deutschen Nationalbibliothek am 10. und 11. Oktober 2019 (2020) 0.00 
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    Type
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  3. Suominen, O.; Koskenniemi, I.: Annif Analyzer Shootout : comparing text lemmatization methods for automated subject indexing (2022) 0.00 
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    Abstract
    Automated text classification is an important function for many AI systems relevant to libraries, including automated subject indexing and classification. When implemented using the traditional natural language processing (NLP) paradigm, one key part of the process is the normalization of words using stemming or lemmatization, which reduces the amount of linguistic variation and often improves the quality of classification. In this paper, we compare the output of seven different text lemmatization algorithms as well as two baseline methods. We measure how the choice of method affects the quality of text classification using example corpora in three languages. The experiments have been performed using the open source Annif toolkit for automated subject indexing and classification, but should generalize also to other NLP toolkits and similar text classification tasks. The results show that lemmatization methods in most cases outperform baseline methods in text classification particularly for Finnish and Swedish text, but not English, where baseline methods are most effective. The differences between lemmatization methods are quite small. The systematic comparison will help optimize text classification pipelines and inform the further development of the Annif toolkit to incorporate a wider choice of normalization methods.
    Type
    a
  4. Kasprzik, A.: Aufbau eines produktiven Dienstes für die automatisierte Inhaltserschließung an der ZBW : ein Status- und Erfahrungsbericht. (2023) 0.00 
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  5. Mödden, E.; Dreger, A.; Hommes, K.P.; Mohammadianbisheh, N.; Mölck, L.; Pinna, L.; Sitte-Zöllner, D.: ¬Der Weg zur Gründung der AG Erschließung ÖB-DNB und die Entwicklung eines maschinellen Verfahrens zur Verschlagwortung der Kinder- und Jugendliteratur mit GND-Vokabular (2020) 0.00 
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