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Nutzung von Transfer-Learning basierten Ansätzen zur Textanalyse

Nutzung von Transfer-Learning basierten Ansätzen zur Textanalyse

          
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About the Book

Studienarbeit aus dem Jahr 2020 im Fachbereich Informatik - Sonstiges, Note: 1,1, Hochschule München, Veranstaltung: Datenbanken, Sprache: Deutsch, Abstract: Transfer Learning schafft es durch vortrainierte Modelle komplexe neuronale Zusammenhänge abzubilden. Aus einem bereits fertig trainierten Korpus können Ergebnisse auf neue Themengebiete übertragen werden. Anfangs wurde diese Technologie hauptsächlich zur Bildanalyse eingesetzt, doch in den letzten Jahren gewann Transfer Learning auch im Bereich der Textverarbeitung immer mehr an Bedeutung. Das Ziel dieser Forschungsarbeit ist es, eine detaillierte Vermittlung des aktuellen Stands der Wissenschaft zu liefern. Hierbei wird neben einem grundlegenden Überblick über künstliche neuronale Netzwerke zunächst ein Verständnis für Transfer Learning vermittelt, in dem die Einsatzgebiete, Herausforderungen und Risiken unterschiedlicher Transfer Learning Strategien aufgezeigt werden. Insbesondere wird die spezielle Anwendung BERT ausführlich evaluiert und nicht nur deren Entwicklung, sondern auch die Funktionsweise erörtert. BERT, Bidirectional Encoder Representations from Transformers, wurde 2018 von Google veröffentlicht und gilt als Fundament für eine neue Art der Textverarbeitung. Damit BERT im Kontext der Textanalyse eine objektive Einordnung erfährt, werden differenzierte Modelle wie Doc2Vec und GloVe unter die Lupe genommen, um zusätzliche Blickwinkel auf Transfer Learning zu erschließen. Diese einzelnen Modelle können mit Hilfe von Benchmark-Methoden untereinander verglichen werden. Dabei werden neben dem General Language Understanding Evaluation (GLUE) Benchmark, ebenfalls Analyseaufgaben, wie SQuAD und SWAG erläutert und die einzelnen Modelle auf deren Rangliste miteinander in Konkurrenz gesetzt. Diese Benchmark-Methoden sind ein Zusammenschluss von einzelnen Ressourcen, die zum trainieren, analysieren und bewerten von Systemen zum Verständnis natürlicher Sprachen, wie BERT, dienen. Abgerundet wird die Studien


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Product Details
  • ISBN-13: 9783346276896
  • Publisher: Grin Verlag
  • Publisher Imprint: Grin Verlag
  • Height: 210 mm
  • No of Pages: 52
  • Spine Width: 3 mm
  • Width: 148 mm
  • ISBN-10: 3346276899
  • Publisher Date: 10 Oct 2020
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Weight: 82 gr


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