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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://repozitorij.uni-lj.si/IzpisGradiva.php?id=180084"><dc:title>Generative AI in pragmatics</dc:title><dc:creator>Todorović,	Tadej	(Avtor)
	</dc:creator><dc:creator>Flogie,	Andrej	(Avtor)
	</dc:creator><dc:creator>Hari,	Daniel	(Avtor)
	</dc:creator><dc:subject>pragmatics</dc:subject><dc:subject>speech act analyses</dc:subject><dc:subject>ChatGPT</dc:subject><dc:subject>DeepSeek</dc:subject><dc:subject>Gemini</dc:subject><dc:description>This study explores the feasibility of using generative AI (ChatGPT, Gemini, and DeepSeek) to automate speech act annotation in Harold Pinter's play The Birthday Party. Three chatbots - ChatGPT, Gemini, and DeepSeek - were tested under three scenarios varying in the amount of theoretical material provided. Each chatbot's output was compared to a manually annotated reference via a Python script measuring classification accuracy. Scenario 2 produced the highest accuracy overall (75-82%), while Scenario 1 underperformed, owing to incorrect reliance on external typologies, and Scenario 3 showed signs of overfitting. ChatGPT o1 emerged as the most accurate model, achieving 82% accuracy in Scenario 2. The findings suggest that GenAI chatbots can serve as valuable preliminary annotators when good prompt-engineering and well-curated theoretical material are provided. Future research could extend this methodology to more context-dependent texts, further refining prompt-engineering strategies and exploring larger linguistic corpora.</dc:description><dc:date>2025</dc:date><dc:date>2026-03-02 12:38:45</dc:date><dc:type>Članek v reviji</dc:type><dc:identifier>180084</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>
