Medicinal Plant Research 2025, Vol.15, No.6, 244-253 http://hortherbpublisher.com/index.php/mpr 251 The main limitation of this study lies in the size and scope of the corpus. Many advanced linguistic and AI models for Traditional Chinese Medicine and ginseng research are limited by the lack of large, annotated corpora, which affects the generalizability and robustness of the results. The diversity of text types and inconsistent definitions of entities in TCM literature also add complexity to systematic analysis. Rhetorical annotation, especially in identifying discourse steps and functions, involves a certain level of subjectivity. Differences in annotation standards and the interpretive nature of rhetorical analysis may lead to inconsistencies, affecting the reliability of cross-study comparisons. Future research should focus on combining corpus linguistics with AI-assisted text analysis. Large language models and advanced natural language processing can help improve human ability in entity recognition, knowledge extraction, and discourse analysis in traditional Chinese medicine studies. Building domain-specific corpora and evaluation benchmarks will be key to improving model performance and interpretability. In the effort to internationalize and enhance the influence of traditional Chinese medicine and ginseng research, it is also important to standardize terminology, improve translation quality, and promote cross-cultural academic exchange. By strengthening digital resource development, promoting open-access corpora, and adopting multilingual publishing strategies, the communication gap between Chinese and foreign research communities can be reduced, helping the global spread and wider recognition of traditional Chinese medicine scholarship. Author’s contributions AYY conceived and conducted the research, completed the literature review and data analysis, and drafted the initial version of the manuscript. YMY participated in the writing and critical revision of the manuscript and contributed to the refinement of the research framework. JXY assisted in the collection and organization of literature materials and participated in revising and improving the manuscript. All authors read and approved the final manuscript. Acknowledgements This study was supported by the Higher Education Teaching Project of Jilin Province:“Research and Practice on Innovative Digital Teaching Paths for Traditional Chinese Culture in Translation Education of Applied Undergraduate Colleges” (Project No. JGJX25D1126), the Social Science Project of the Jilin Provincial Department of Education: “A Study on the Translation Ethics and Cross-cultural Comparison of Jilin’s Ginseng and Deer Antler Culture”, and the Jilin Provincial Department of Science and Technology Project: “Research on Translation and Dissemination Paths of Jilin Ginseng Culture from the Perspective of Cultural and Tourism Integration”. References Arring N.M., Millstine D., Marks L.A., and Nail L.M., 2018, Ginseng as a treatment for fatigue: A systematic review, The Journal of Alternative and Complementary Medicine, 24(7): 624-633. https://doi.org/10.1089/acm.2017.0361 Bhatnagar V., Duari S., and Gupta S.K., 2022, Quantitative discourse cohesion analysis of scientific scholarly texts using multilayer networks, IEEE Access, 10: 88538-88557. https://doi.org/10.1109/access.2022.3198952 Chai C.P., 2023, Comparison of text preprocessing methods, Natural Language Engineering, 29(3): 509-553. https://doi.org/10.1017/S1351324922000213 Chen C., and Zhang L.J., 2017, An intercultural analysis of the use of hedging by Chinese and Anglophone academic English writers, Applied Linguistics Review, 8(1): 1-34. https://doi.org/10.1515/applirev-2016-2009 Chen Q., Ai N., Liao J., Shao X., Liu Y., and Fan X., 2017, Revealing topics and their evolution in biomedical literature using Bio-DTM: A case study of ginseng, Chinese Medicine, 12(1): 27. https://doi.org/10.1186/s13020-017-0148-7 Dai Q., 2022, Construction of English and American literature corpus based on machine learning algorithm, Computational Intelligence and Neuroscience, 2022: 9773452. https://doi.org/10.1155/2022/9773452 Deng Z., Ali A.M., and Zin Z.B.M., 2025, Investigating methodological trends of hedging strategies in academic discourse: A systematic literature review, World Journal of English Language, 15(5): 322-340. https://doi.org/10.5430/wjel.v15n5p322
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