Classifying Speech Acts in Political Communication: A Transformer-based Approach with Weak Supervision and Active Learning

• Autorzy: Schmidt Klaus , Niekler Andreas , Kantner Cathleen , Burghardt Manuel

Identyfikatory

DOI

10.15439/2023F3485

• Języki publikacji: EN

Abstrakty

EN

We present a study on the automatic classification of speech acts in the domain of political communication, based on J. R. Searle's classification of illocutionary acts. Our research involves creating a dataset using the US State of the Union corpus and the UN General Debate corpus (UNGD) as data sources. To overcome limited labeled data, we employ a combination of weak supervision and active learning techniques for dataset creation and model training. Through various experiments, we investigate the influence of external and internal factors on speech act classification. In addition, we discuss the potential for further analysis of speech act usage, using the trained model on the UNGD corpus. The findings demonstrate the effectiveness of Transformer-based models for automatic speech act classification, highlight the benefits of weak supervision and active learning for dataset creation and model training, and underscore the potential for large-scale statistical analysis of speech act usage in the domain of political communication.

Słowa kluczowe

EN

computer science; analytical model; statistical analysis; soft sensor; computational modeling; transformer

PL

informatyka; model analityczny; analiza statystyczna; czujnik; modelowanie obliczeniowe; transformator

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 739--748
• Opis fizyczny: Bibliogr. 32 poz., wykr., tab., il.

Twórcy

autor

Schmidt Klaus

• Leipzig University

autor

Niekler Andreas

• Leipzig University

autor

Kantner Cathleen

• University of Stuttgart

autor

Burghardt Manuel

• Leipzig University

Bibliografia

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Uwagi

1. Thematic Tracks Regular Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).