정치적 담화에서의 개념적 은유 분석을 위한 AI 모델의 활용과 한계: GPT-4를 통한 실험적 연구
Investigating the applications and limitations of GPT-4 in conceptual metaphor analysis in political discourse: An experimental approach
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This study used the GPT-4 model to analyze conceptual metaphors in the political discourse of the 2024 U.S. presidential election candidate TV debates and evaluate the AI's language processing capabilities and limitations. The experimental results showed a 13.56% error rate and 86.44% precision in the overall analysis. For the partial correct items, which accounted for 46.61% of the total correct results, the error rates were as follows: TD error rate 17.80%, SD error rate 9.32%, TD&SD error rate 8.47%, and mixed metaphor error rate 11.02%. In addition, the model missed mixed metaphors, showing the limitations of metaphor detection. The study shows that while AI is highly accurate at analyzing conceptual metaphors, it does not fully understand complex figurative language due to differences in contextual analysis and human cognitive understanding. The results provide academic insights into AI's language processing capabilities and suggest directions for further research.
Abstract
This study used the GPT-4 model to analyze conceptual metaphors in the political discourse of the 2024 U.S. presidential election candidate TV debates and evaluate the AI's language processing capabilities and limitations. The experimental results showed a 13.56% error rate and 86.44% precision in the overall analysis. For the partial correct items, which accounted for 46.61% of the total correct results, the error rates were as follows: TD error rate 17.80%, SD error rate 9.32%, TD&SD error rate 8.47%, and mixed metaphor error rate 11.02%. In addition, the model missed mixed metaphors, showing the limitations of metaphor detection. The study shows that while AI is highly accurate at analyzing conceptual metaphors, it does not fully understand complex figurative language due to differences in contextual analysis and human cognitive understanding. The results provide academic insights into AI's language processing capabilities and suggest directions for further research.
참고문헌 (17)
- 백지연, "자연어처리 딥러닝 모델의 은유 이해도 분석", 한국정보과학회 학술발표논문집, pp.1388~1390 (2020)
- Banarescu, L., "Abstract meaning representation for sembanking", Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp.178~186 (2013)
- Croft, W., "Cognitive linguistics" (2004), Cambridge University Press
- Glucksberg, S., "Understanding metaphorical comparisons : Beyond similarity", Psychological Review, 97(1), pp.3~18 (1990)
- Handy, C., "The citizen corporation", Harvard Business Review, 75(5), pp.26~27 (1997)
- Harser, V., "Metaphor, metonymy, and experientialist philosophy: Challenging cognitive semantics" (1973), Walter de Gruyter GmbH & Co
- "GPT-4 vs. GPT-3.5: A concise showdown" (2023)[URL]
- Lakoff, G., "Metaphors we live by" (2003), University of Chicago Press
- "READ: Harris-trump presidential debate transcript" (2024)[URL]
- Ouyang, L., "Training language models to follow instructions with human feedback", Advances in Neural Information Processing Systems, 35, pp.27730~27744 (2022)
- "Improving language understanding by generative pre-training" (2018)[URL]
- Radford, A., "Language models are unsupervised multitask learners", OpenAI blog, 1(8), pp.9~ (2019)
- Raman, S. R., "Metaphors and managers : New ways of thinking and seeing", Vikalpa, 27(3), pp.3~12 (2002)
- Shim, H., "A cognitive semantics of politics : With focus on debates between the U. S. presidential candidates", The Journal of Linguistic Science, 96, pp.127~144 (2021)
- Vaswani, A., "Attention is all you need", Advances in Neural Information Processing Systems (2017)