JCERJournal of Contemporary Educational Research2208-84662208-8474Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jcer.v7i12.5713ArticleCorpus-Driven Analysis of Conceptual Metaphor in Artificial Intelligence Language: A Sample of ChatGPT-Written Speecheshttps://artdesignp.com/journal/JCER/7/12/10.26689/jcer.v7i12.5713YangYang20237122023-12-25Based on Conceptual Metaphor Theory (CMT), this paper creates a tiny corpus of ChatGPT-written speeches. Through employing a corpus-driven approach, this study analyzes the identification and utilization of conceptual metaphors in artificial intelligence (AI) languages. The AI demonstrated its capacity to utilize metaphors in the metaphoric corpora through the display of diversity, non-arbitrariness, repetition, and intersectionality in the selection of source domains. It often uses vocabulary combinations with clear similarities to establish metaphorical meaning. In the literal sense, the outcomes of metaphor identification by artificial intelligence differ significantly from those of humans. Therefore, there is a need to develop advanced automatic models for identifying metaphors in order to enhance the precision of metaphor identification consistently.

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