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Die Anwendung von Machine Learning zur Gewinnung von Erkenntnissen aus Dokumentenstapeln

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Zusammenfassung

„Document Understanding“ ist das tiefe Verständnis eines Textes. Im Kern geht es um die Konvertierung von unstrukturierten Daten in Informationen und für Unternehmen gleichermaßen um die die Einhaltung von Governance- und Compliance-Richtlinien. Zum Einsatz kommt zumeist eine Sammlung von verschiedenen Methoden, zu denen unter anderem die Document Classification oder auch die Entity Extraction gehören. Viele Ansätze beruhen auf regelbasierten Systemen respektive auf statistischen Verfahren.

Der Einsatz von Machine Learning zur massenhaften Erschließung unstrukturierter Dokumente eröffnet neue Wege, um unter anderem Beziehungen zwischen Dokumenten sichtbar zu machen. ML ermöglicht Vorhersagen zur Dokumentenklassifizierung oder etwa die Extraktion von Wissen aus Textpassagen, Grafiken oder Feldern jenseits einfacher Mustererkennung. ML stellt Möglichkeiten einer semantischen Suche über Dokumente hinweg zur Verfügung und legt den Grundstein für erweiterte Analysen beispielsweise der Anomalieerkennung.

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Notes

  1. 1.

    Kurzbezeichnung für einen Trainingsdatensatz bereitgestellt durch die Message Understanding Conferences.

  2. 2.

    https://www.tensorflow.org/tutorials/keras/text_classification_with_hub

  3. 3.

    Vielfach erfolgt eine Transkription für Audio- und Video-Daten durch das entsprechende System automatisch.

  4. 4.

    https://github.com/cayleygraph/cayley

  5. 5.

    https://patents.google.com/patent/US20170011116A1/en

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Ebener, S. (2020). Die Anwendung von Machine Learning zur Gewinnung von Erkenntnissen aus Dokumentenstapeln. In: Buchkremer, R., Heupel, T., Koch, O. (eds) Künstliche Intelligenz in Wirtschaft & Gesellschaft. FOM-Edition. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-29550-9_15

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