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DeInStance: Creating and Evaluating a German Corpus for Fine-Grained Inferred Stance Detection

Göhring, Anne; Klenner, Manfred; Conrad, Sophia (2021). DeInStance: Creating and Evaluating a German Corpus for Fine-Grained Inferred Stance Detection. In: 17th Conference on Natural Language Processing (KONVENS 2021), Düsseldorf, 6 September 2021 - 9 September 2021. ACL Anthology, 213-217.

Abstract

We introduce deInStance, a corpus of 1000 politicians’ answers in German (de) containing sentences labeled with explicitly expressed and inferred stances - pro and con relations - by 3 annotators. They achieved an acceptable inter-rater agreement given the inherent subjective nature of the task. A first baseline, a fine-tuned BERT-based token classifier, achieved F1-scores of around 70% . Our focus is on the difficult subclass of sentences comprising only non-polar words, but still with an (implicit) pro or con perspective of the writer.

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:9 September 2021
Deposited On:21 Oct 2021 13:01
Last Modified:16 Sep 2024 13:12
Publisher:ACL Anthology
Number of Pages:5
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://aclanthology.org/2021.konvens-1.20
Project Information:
  • Funder: SNSF
  • Grant ID: 105215_179302
  • Project Title: Sentimentinferenz: Perspektivierung und Quantifikation
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  • Language: English
  • Licence: Creative Commons: Attribution 3.0 Unported (CC BY 3.0)

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