How to do politics with words: Investigating speech acts in parliamentary debates


Reinig, Ines ; Rehbein, Ines ; Ponzetto, Simone Paolo


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URL: https://aclanthology.org/2024.lrec-main.727/
URN: urn:nbn:de:bsz:180-madoc-676922
Document Type: Conference or workshop publication
Year of publication: 2024
Book title: The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) : main conference proceedings, 20-25 May, 2024, Torino, Italia
Page range: 8287-8300
Conference title: LREC @ Coling 2024
Location of the conference venue: Torino, Italy
Date of the conference: 20-25.05.2024
Publisher: Calzolari, Nicoletta ; Kan, Min-Yen ; Hoste, Veronique ; Lenci, Alessandro ; Sakti, Sakriani ; Xue, Nianwen
Place of publication: Torino, Italia
Publishing house: ELRA and ICCL
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Sonstige - Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik
School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Pre-existing license: Creative Commons Attribution, Non-Commercial 4.0 International (CC BY-NC 4.0)
Subject: 004 Computer science, internet
Keywords (English): speech acts , political text analysis
Abstract: This paper presents a new perspective on framing through the lens of speech acts and investigates how politicians make use of different pragmatic speech act functions in political debates. To that end, we created a new resource of German parliamentary debates, annotated with fine-grained speech act types. Our hierarchical annotation scheme distinguishes between cooperation and conflict communication, further structured into six subtypes, such as informative, declarative or argumentative-critical speech acts, with 14 fine-grained classes at the lowest level. We present classification baselines on our new data and show that the fine-grained classes in our schema can be predicted with an avg. F1 of around 82.0%. We then use our classifier to analyse the use of speech acts in a large corpus of parliamentary debates over a time span from 2003–2023.




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