Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets

Abstract: This article investigates the integration of machine learning in the political claim annotation workflow with the goal to partially automate the annotation and analysis of large text corpora. It introduces the MARDY annotation environment and presents results from an experiment in which the annotation quality of annotators with and without machine learning based annotation support is compared. The design and setting aim to measure and evaluate: a) annotation speed; b) annotation quality; and c) applicability to the use case of discourse network generation. While the results indicate only slight increases in terms of annotation speed, the authors find a moderate boost in annotation quality. Additionally, with the help of manual annotation of the actors and filtering out of the false positives, the machine learning based annotation suggestions allow the authors to fully recover the core network of the discourse as extracted from the articles annotated during the experiment. This is d

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: Veröffentlichungsversion ; begutachtet (peer reviewed) ; In: Politics and Governance ; 8 (2020) 2 ; 326-339

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Mannheim
• (wer): SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
• (wann): 2020
• Urheber: Haunss, Sebastian ; Kuhn, Jonas ; Padó, Sebastian ; Blessing, Andre ; Blokker, Nico ; Dayanik, Erenay ; Lapesa, Gabriella

Weitere Informationen

• DOI: 10.17645/pag.v8i2.2591
• URN: urn:nbn:de:101:1-2022070515360534123824
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 25.03.2025, 13:43 MEZ