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Design of ChatGPT Translation NER Model Fusing Dice Loss and Global Attention

Published: 20 September 2024 Publication History

Abstract

Chat Generative Pre-trained Transformer (ChatGPT) plays an important role in the field of natural language processing, in order to further improve the accuracy of ChatGPT in the field of text translation and extend the application scenarios, the research centers around ChatGPT's natural language processing, a new named entity recognition model is designed by introducing the Dice loss factor and improving the global attention mechanism. The experimental results show that under the same experimental environment, the recall of the model can reach 0.92 and 0.96 when the precision rate is 0.9 and 0.8; and the highest value of F1 value can reach 89.67%. Meanwhile, the loss function curve of the research design model is better than other models in convergence speed and precision, and the improvement strategy of Dice's loss factor has significant optimization effect. The named entity recognition model designed in the study is conducive to improving the readability and semantic consistency of ChatGPT translation, meeting the needs of different scenarios, and providing users with more accurate translation services.

References

[1]
S. S. Biswas, "Potential use of chat gpt in global warming," Annals of biomedical engineering, vol. 51, no. 6, pp. 1126-1127, October 2023.
[2]
S. S. Biswas, "Role of chat gpt in public health," Annals of biomedical engineering, vol. 51, no. 5, pp. 6302-6318, 2023.
[3]
S. AlZu'bi, A. Mughaid, F. Quiam, and S. Hendawi, "Exploring the capabilities and limitations of chatgpt and alternative big language models," In Artificial Intelligence and Applications, vol. 2, no. 1, pp. 28-37, 2024.
[4]
T. Wu, S. He, J. Liu, S. Sun, K. Liu, Q. L. Han, and Y. Tang, "A brief overview of ChatGPT: The history, status quo and potential future development," IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 5, pp. 1122-1136, 2023.
[5]
M. Shidiq, "The use of artificial intelligence-based chat-gpt and its challenges for the world of education; from the viewpoint of the development of creative writing skills," In Proceeding of International Conference on Education, Society and Humanity, vol. 1, no. 1, pp. 353-357, 2023.
[6]
T. J. Chen, "ChatGPT and other artificial intelligence applications speed up scientific writing," Journal of the Chinese Medical Association, vol. 86, no. 4, pp. 351-353, 2023.
[7]
D. L. Mann, "Artificial intelligence discusses the role of artificial intelligence in translational medicine: a JACC: basic to translational science interview with ChatGPT," Basic to Translational Science, vol. 8, no. 2, pp. 221-223, 2023.
[8]
C. Hebbi, and H. R. Mamatha, "Comprehensive dataset building and recognition of isolated handwritten kannada characters using machine learning models," In Artificial Intelligence and Applications, vol. 1, no. 3, pp.179-190, 2023.
[9]
B. D. Lund, T. Wang, N. R. Mannuru, B. Nie, S. Shimray, and Z. Wang, "ChatGPT and a new academic reality: Artificial Intelligence -written research papers and the ethics of the large language models in scholarly publishing," Journal of the Association for Information Science and Technology, vol. 74, no. 5, pp. 570-581, 2023.
[10]
Z. Nasar, S. W. Jaffry, and M. K. Malik, "Named entity recognition and relation extraction: state-of-the-art," ACM Computing Surveys (CSUR), vol. 54, no. 1, pp. 1-39, 2021.
[11]
L. Weber, M. Sänger, J. Münchmeyer, M. Habibi, U. Leser, and A. Akbik, "HunFlair: an easy-to-use tool for state-of-the-art biomedical named entity recognition," Bioinformatics, vol. 37, no. 17, pp. 2792-2794, 2021.
[12]
A. Kumar, and B. Starly, "FabNER: information extraction from manufacturing process science domain literature using named entity recognition," Journal of Intelligent Manufacturing, vol. 33, no. 8, pp. 2393-2407, 2022.
[13]
M. Sung, M. Jeong, Y. Choi, D. Kim, J. Lee, and J. Kang, "BERN2: an advanced neural biomedical named entity recognition and normalization tool," Bioinformatics, vol. 38, no. 20, pp. 4837-4839, 2022.
[14]
P. Preethi, and H. R. Mamatha, "Region-based convolutional neural network for segmenting text in epigraphical images," In Artificial Intelligence and applications, vol. 1, no. 2, pp. 119-127, 2023.
[15]
A. Hamdi, E. L. Pontes, N. Sidere, M. Coustaty, and A. Doucet, "In-depth analysis of the impact of OCR errors on named entity recognition and linking," Natural Language Engineering, vol. 29, no. 2, pp. 425-448, 2023.

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              FAIML '24: Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning
              April 2024
              379 pages
              ISBN:9798400709777
              DOI:10.1145/3653644
              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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              Published: 20 September 2024

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              Author Tags

              1. Attention
              2. Dice loss factor
              3. Loss function
              4. Named entity recognition
              5. Trigger coding

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