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Pipelines für Natural Language Processing und digitale Literaturanalyse in spaCy

Authors: Varachkina, Hanna; Barth, Florian; Dönicke, Tillmann; Biermann, Johannes; Altmann, Friederike; Neitzke, Thorben; Sporleder, Caroline;

Pipelines für Natural Language Processing und digitale Literaturanalyse in spaCy

Abstract

In diesem halbtägigen Workshop stellen wir ein auf spaCy basierendes Pipeline-System für das Natural Language Processing (NLP) vor und erproben mit den Teilnehmer*innen dessen praktische Anwendung, besonders im Hinblick auf Untersuchungsgegenstände der digitalen Literaturanalyse. Der Workshop vermittelt (i) die Grundlagen von spaCy und dessen Kernkomponenten (Tokenisierung, Lemmatisierung, Erkennung von Satz- und Teilsatzgrenzen, Named Entity Recognition, Dependency Parsing), (ii) demonstriert, wie die spaCy-Pipeline an die eigenen Zwecke durch Custom-Komponenten angepasst werden kann (z. B. zur Erkennung von Zeitformen oder Redewiedergabe), und versetzt (iii) die Teilnehmer*innen mit hands-on Praxisbeispielen in die Lage, die Pipeline-Komponenten zur Erschließung der linguistischen und narrativen Struktur eines Textes im Rahmen eigener Projekte kompetent auszuwählen, anzuwenden, zu erweitern und die Ergebnisse zu beurteilen. Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture.

Keywords

Pipelines, Strukturanalyse, DHd2023, Programmierung, Literatur, Textanalyse, Python, Inhaltsanalyse, Text

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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