Building an Intelligent Annotation and Personalized Learning Platform for Ancient Chinese Poetry by Integrating ERNIE—Stu and Transformer—XL
Main Article Content
Abstract
Ancient Chinese texts contain important records of cultural heritage, yet existing learning systems still exhibit insufficient semantic coherence, limited word-sense disambiguation, and weak personalized recommendation capability. This paper proposes an intelligent annotation and personalized learning platform for ancient poetry by integrating ERNIE-Stu and Transformer-XL. ERNIE-Stu is used for word embedding, entity recognition, and knowledgegraph-enhanced word-sense disambiguation, while Transformer-XL captures long-range semantic dependencies through segment-level recurrence. Based on syntactic dependency analysis, the platform generates hierarchical context -aware annotations and constructs learner representations from interaction features. Semantic similarity and a timedecay mechanism are further combined to support adaptive recommendation. The semantic fusion framework also provides methodological reference for heterogeneous information understanding and intelligent interpretation in electromagnetic information processing and knowledge-driven communication systems. Experimental results show F1- scores of 0.91 for content-word annotation and 0.87 for function-word annotation, a BERTScore-F1 of 0.798 for longtext discourse similarity, NDCG@5 above 0.70, and CTR above 0.45, confirming improvements in annotation completeness, learning adaptability, and digital cultural-knowledge dissemination.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
E. Du Plooy, D. Casteleijn, and D. Franzsen, “Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement,” Heliyon, vol. 10, no. 21, pp. 1-24, 2024, doi: 10.1016/j.heliyon.2024.e39630.
A. Sikarskie, “Digital research methods in fashion and textile studies,” London, UK: Bloomsbury Publishing; 2020.
Y. Wei, L. Hu, Y. Zhu, J. Zhao, and B. Wu, “Knowledge-guided transformer for joint theme and emotion classification of Chinese classical poetry,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 1, no. 1, pp. 1-12, 2024, doi: 10.1109/TASLP.2024.3487409.
A. P. Wibawa and F. Kurniawan, “Advancements in natural language processing: Implications, challenges, and future directions,” Telematics and Informatics Reports, vol. 16, no. 1, pp. 100173-100190, 2024, doi: 10.1016/j.teler.2024.100173.
C. Pramartha, G. D. Saputra, I. W. Supriana, I. G. Putra, I. W. Arka, and M. A. Raharja, “Enabling Cultural Heritage Preservation: A Semantic Web Prototype for Digitizing and Documenting Balinese Woven Fabric Artifacts,” Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023); 21-22 November 2023; Denpasar, Indonesia (Hybrid), 2024, doi: 10.2991/978-94-6463-413-6_12.
E. Alba, M. Gaitán, A. León, D. Mladeni´c, and J. Brank, “Weaving words for textile museums: the development of the linked SILKNOW thesaurus,” Heritage Science, vol. 10, no. 1, pp. 59, 2022, doi: 10.21203/rs.3.rs-1043107/v1.
M. C. Hyer, “Textiles and Textile Imagery in Early Medieval English Literature: Traditions and Contexts,” Suffolk, UK: Boydell & Brewer; 2025, doi: 10.1515/9781805436126.
C. Xiao, P. Zhang, X. Han, G. Xiao, Y. Lin, Z. Zhang, et al., “Infllm: Training-free long-context extrapolation for llms with an efficient context memory,” Advances in Neural Information Processing Systems, vol. 37, no. 1, pp. 119638-119661, 2024, doi: 10.52202/079017-3801.
C. C. Y. Yang and H. Ogata, “Personalized learning analytics intervention approach for enhancing student learning achievement and behavioral engagement in blended learning,” Education and Information Technologies, vol. 28, no. 3, pp. 2509-2528, 2023, doi: 10.1007/s10639-022-11291-2.
H. Yaseen, A. S. Mohammad, N. Ashal, H. Abusaimeh, A. Ali, and A. A. A. Sharabati, “The impact of adaptive learning technologies, personalized feedback, and interactive AI tools on student engagement: The moderating role of digital literacy,” Sustainability, vol. 17, no. 3, pp. 1133-1160, 2025, doi: 10.3390/su17031133.
L. C. Wang, X. Y. Zeng, L. Koehl, and Y. Chen, “Intelligent fashion recommender system: Fuzzy logic in personalized garment design,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 1, pp. 95-109, 2014, doi: 10.1109/THMS.2014.2364398.
S. Chakraborty, M. S. Hoque, and S. M. Surid, “A COMPREHENSIVE REVIEW ON IMAGE BASED STYLE PREDICTION AND ONLINE FASHION RECOMMENDATION,” Journal of Modern Technology & Engineering, vol. 5, no. 3, pp. 212-233, 2020, [Online]. Available: https://jomardpublishing.com/UploadFiles/Files/journals/JTME/V5N3/ChakrabortyS.pdf.
M. Mejeh and M. Rehm, “Taking adaptive learning in educational settings to the next level: Leveraging natural language processing for improved personalization,” Educational Technology Research and Development, vol. 72, no. 3, pp. 1597-1621, 2024, doi: 10.1007/s11423-024-10345-1.
Y. Yao and H. González-Vélez, “AI-Powered System to Facilitate Personalized Adaptive Learning in Digital Transformation,” Applied Sciences, vol. 15, no. 9, pp. 4989-5016, 2025, doi: 10.3390/app15094989.
J. Hou and S. Zhang, “Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERTenhanced Ensemble Learning Approach,” ACM Journal on Computing and Cultural Heritage, vol. 17, no. 4, pp. 1-19, 2025, doi: 10.1145/3685679.
Z. Li, Y. Jian, Z. Xue, Y. Zheng, M. Zhang, and Y. Zhang, “Text-enhanced knowledge graph representation learning with local structure,” Information Processing & Management, vol. 61, no. 5, pp. 103797-103813, 2024, doi: 10.1016/j.ipm.2024.103797.
H. Yu, C. Gao, X. Li, and L. Zhang, “Ancient Chinese Poetry Collation Based on BERT,” Procedia Computer Science, vol. 242, no. 1, pp. 1171-1178, 2024, doi: 10.1016/j.procs.2024.08.179.
Q. Sun, K. Zhang, L. Lv, X. Li, K. Huang, and T. Zhang, “Joint extraction of entities and overlapping relations by improved graph convolutional networks,” Applied Intelligence, vol. 52, no. 5, pp. 5212-5224, 2022, doi: 10.1007/s10489-021-02667-x.
D. Du, J. Ding, and Y. Liu, “Knowledge graph construction of Chinese embroidery evolution based on associating cultural space and critical incidents under intangible cultural heritage,” The Electronic Library, vol. 43, no. 3, pp. 283-302, 2025, doi: 10.1108/EL-02-2024-0036.
A. Jana and R. Rout, “Sambalpuri Sarees in the Digital Loom: Metadata Schema and Natural Language Query Interface Design,” Journal of Library Metadata, vol. 1, no. 1, pp. 1-20, 2026, doi: 10.1080/19386389.2026.2636260.
L. Gan, L. Hu, X. Tan, and X. Du, “TBNF: A transformer-based noise filtering method for Chinese long-form text matching,” Applied Intelligence, vol. 53, no. 19, pp. 22313-22327, 2023, doi: 10.1007/s10489-023-04607-3.
S. Zhang, P. Wang, Z. Li, J. Hou, and Q. Hu, “Confidence-based Syntax encoding network for better ancient Chinese understanding,” Information Processing & Management, vol. 61, no. 3, pp. 103616-103632, 2024, doi: 10.1016/j.ipm.2023.103616.
C. Yu, H. Xue, Y. Jiang, L. An, and G. Li, “A simple and efficient text matching model based on deep interaction,” Information Processing & Management, vol. 58, no. 6, pp. 102738-102754, 2021, doi: 10.1016/j.ipm.2021.102738.
Y. Wei, M. Li, Y. Zhu, Y. Xu, Y. Li, and B. Wu, “A diachronic language model for long-time span classical Chinese,” Information Processing & Management, vol. 62, no. 1, pp. 103925-103941, 2025, doi: 10.1016/j.ipm.2024.103925.
C. Chen, “A Personalized Recommendation Method for Ancient Chinese Literary Works Based on a Collaborative Filtering Algorithm,” Mobile Information Systems, vol. 2022, no. 1, pp. 4460628-4460635, 2022, doi: 10.1155/2022/4460628.
Y. Ma and G. Yu, “Construction of a Teaching Platform of Modern and Contemporary Chinese Literature From the Perspective of Diversified Intelligence,” International Journal of e-Collaboration (IJeC), vol. 20, no. 1, pp. 1-18, 2024, doi: 10.4018/ijec.357638.
G. Lv, “Personalized Recommendation Algorithm of Literary Works Based on Annotated Corpus,” Mobile Information Systems, vol. 2022, no. 1, Art. no. 5198612, 2022, doi: 10.1155/2022/5198612.
S. Shirkhani, “Image-based fashion recommender systems,” 2021, [Online]. Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1603327&dswid=-636.
G. Wu, Z. Lu, X. Zhuo, X. Bao, and X. Wu, “Semantic fusion enhanced event detection via multi-graph attention network with skip connection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 3, pp. 931-941, 2023, doi: 10.1109/TETCI.2022.3229414.
S. Y. Cui, B. W. Yu, X. Cong, T. W. Liu, Q. F. Tan, and J. Q. Shi, “Label-aware Chinese event detection with heterogeneous graph attention network,” Journal of Computer Science and Technology, vol. 39, no. 1, pp. 227-242, 2024, doi: 10.1007/s11390-023-1541-6.
M. Yasunaga, A. Bosselut, H. Ren, X. Zhang, C. D. Manning, P. S. Liang, et al., “Deep bidirectional languageknowledge graph pretraining,” Advances in Neural Information Processing Systems, vol. 35, no. 1, pp. 37309-37323, 2022.
J. Huang and S. Liu, “Lexicon-enhanced transformer with spatial-aware integration for Chinese named entity recognition,” Complex & Intelligent Systems, vol. 11, no. 8, pp. 1-17, 2025, doi: 10.1007/s40747-025-01953-2.
J. Cheng, W. Tong, and W. Yan, “Capsule network improved multi-head attention for word sense disambiguation,” Applied Sciences, vol. 11, no. 6, pp. 2488-2502, 2021, doi: 10.3390/app11062488.
S. Kwon, D. Oh, and Y. Ko, “Word sense disambiguation based on context selection using knowledge-based word similarity,” Information Processing & Management, vol. 58, no. 4, pp. 102551-102567, 2021, doi: 10.1016/j.ipm.2021.102551.
X. Chen, P. Cong, and S. Lv, “A long-text classification method of Chinese news based on BERT and CNN,” IEEE Access, vol. 10, no. 1, pp. 34046-34057, 2022, doi: 10.1109/ACCESS.2022.3162614.
T. Yang, L. Hu, C. Shi, H. Ji, X. Li, and L. Nie, “HGAT: Heterogeneous graph attention networks for semi-supervised short text classification,” ACM Transactions on Information Systems (TOIS), vol. 39, no. 3, pp. 1-29, 2021, doi: 10.1145/3450352.