
(2) * Aji Prasetya Wibawa

(3) Wahyu Sakti Gunawan Irianto

(4) Anusua Ghosh

(5) Leonel Hernandez

*corresponding author
AbstractThis study explores the narrative structures of Hans Christian Andersen's fairy tales by analyzing event relationships using a combination of BERT (Bidirectional Encoder Representations from Transformers) and Graph Convolutional Networks (GCN). The research begins with the extraction of key events from the tales using BERT, leveraging its advanced contextual understanding to accurately identify and classify events. These events are then modeled as nodes in a graph, with their relationships represented as edges, using GCNs to capture complex interactions and dependencies. The resulting event relationship graph provides a comprehensive visualization of the narrative structure, revealing causal chains, thematic connections, and non-linear relationships. Quantitative metrics, including event extraction accuracy (92.5%), relationship precision (89.3%), and F1 score (90.8%), demonstrate the effectiveness of the proposed methodology. The analysis uncovers recurring patterns in Andersen's storytelling, such as linear event progressions, thematic contrasts, and intricate character interactions. These findings not only enhance our understanding of Andersen's narrative techniques but also showcase the potential of combining BERT and GCN for literary analysis. This research bridges the gap between computational linguistics and literary studies, offering a data-driven approach to narrative analysis. The methodology developed here can be extended to other genres and domains, paving the way for further interdisciplinary research. By integrating state-of-the-art NLP models with graph-based machine learning techniques, this study advances our ability to analyze and interpret complex textual data, providing new insights into the art of storytelling
KeywordsEvent relationships; Andersen's Fairy Tales; BERT; Graph Convolutional Network(GCN); Narrative analysis
|
DOIhttps://doi.org/10.31763/sitech.v5i1.1810 |
Article metrics10.31763/sitech.v5i1.1810 Abstract views : 158 | PDF views : 33 |
Cite |
Full Text![]() |
References
N. H. Varnier and R. F. de Lima Rodrigues, “A contribuição dos contos de fadas: um percurso entre o imaginário e a consciência de si na infância,†Res. Soc. Dev., vol. 9, no. 10, 2020, doi: 10.33448/RSD-V9I10.8242. [2] A. M. Jorge, R. Campos, A. Jatowt, and S. Bhatia, “The 2nd International Workshop on Narrative Extraction from Text: Text2Story 2019,†2019, pp. 389–393, doi: 10.1007/978-3-030-15719-7_54. [3] T. L. M. Suryanto, A. P. Wibawa, H. Hariyono, and A. Nafalski, “Comparative Performance of Transformer Models for Cultural Heritage in NLP Tasks,†Adv. Sustain. Sci. Eng. Technol., vol. 7, no. 1, p. 0250115, Jan. 2025, doi: 10.26877/asset.v7i1.1211. [4] M. A. N. A. Latif, E. Pratiwi, and A. Prameswari, “Analyzing Early Childhood Fairy Tales: Looking for Studies on the Local Wisdom of Madurese Society,†Deleted J., vol. 10, no. 1, pp. 11–18, 2024, doi: 10.14421/al-athfal.2024.101-02. [5] D. E. Cahyani, A. P. Wibawa, D. D. Prasetya, L. Gumilar, F. Akhbar, and E. R. Triyulinar, “Emotion Detection in Text Using Convolutional Neural Network,†in 2022 International Conference on Electrical and Information Technology (IEIT), Sep. 2022, pp. 372–376, doi: 10.1109/IEIT56384.2022.9967913. [6] L. He, Q. Zhang, J. Duan, and H. Wang, “An Open-Domain Event Extraction Method Incorporating Semantic and Dependent Syntactic Information,†Appl. Sci., vol. 13, no. 13, p. 7942, Jul. 2023, doi: 10.3390/app13137942. [7] P. Zhu, Z. Wang, H. Wang, S. Li, and G. Zhou, “Event Detection with Document Structure and Graph Modelling,†in Natural Language Processing and Chinese Computing, Springer International Publishing, 2020, pp. 593–603, doi: 10.1007/978-3-030-60450-9_47. [8] T. Vo, “SynSeq4ED: A Novel Event-Aware Text Representation Learning for Event Detection,†Neural Process. Lett., vol. 54, no. 1, pp. 227–249, Feb. 2022, doi: 10.1007/s11063-021-10627-2. [9] Y. Chen, T. Chen, S. Ebner, A. S. White, and B. Van Durme, “Reading the Manual: Event Extraction as Definition Comprehension,†in Proceedings of the Fourth Workshop on Structured Prediction for NLP, 2020, pp. 74–83, doi: 10.18653/v1/2020.spnlp-1.9. [10] Agata Cybulska and P. Vossen, “Historical Event Extraction from Text,†in 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, 2011, pp. 39–43. [Online]. Available at: https://aclanthology.org/W11-1506/. [11] A. R. Hofer, “Sources: The Greenwood Encyclopedia of Folktales and Fairy Tales,†Ref. User Serv. Q., vol. 48, no. 1, pp. 94–95, Sep. 2008, doi: 10.5860/rusq.48n1.94. [12] H. C. Andersen, Andersen’s Fairy Tales, 1st ed., no. January 1999. Guttenberg, p. 36, 1999. [Online]. Available at: https://www.andersenstories.com/en/andersen_fairy-tales/list. [13] B. Santana, R. Campos, E. Amorim, A. Jorge, P. Silvano, and S. Nunes, A survey on narrative extraction from textual data, vol. 56, no. 8. pp, 8393–8435, Springer Netherlands, 2023, doi: 10.1007/s10462-022-10338-7. [14] A.-U.-N. Fatima, H. Ahmad, M. Ahmad, W. Ahmad, and N. Faisal, “Extraction of Temporal Events Frequency from Online News Channels,†in 2020 30th International Conference on Computer Theory and Applications (ICCTA), pp. 1-25, Dec. 2020, doi: 10.1109/iccta52020.2020.9477659. [15] A. B. P. Utama, A. P. Wibawa, A. N. Handayani, W. S. G. Irianto, Aripriharta, and A. Nyoto, “Improving Time-Series Forecasting Performance Using Imputation Techniques in Deep Learning,†in 2024 International Conference on Smart Computing, IoT and Machine Learning (SIML), Jun. 2024, pp. 232–238, doi: 10.1109/SIML61815.2024.10578273. [16] H. Zhang, H. Song, S. Wang, and B. Xu, “Bert (A Bert-Based End-To-End Model For Chinese Document-Level Event Extraction),†in Proceedings of the 19th Chinese National Conference on Computational Linguistics, 2020, pp. 390–401. [Online]. Available at: https://aclanthology.org/2020.ccl-1.36/. [17] Zecheng Zhang, Yuncheng Wu, and Zesheng Wang, “A Survey of Open Domain Event Extraction.†p.30, 2020. [Online]. Available at: https://pdfs.semanticscholar.org. [18] H. M. D. Trong, N. T. Ngo, L. Van, and T. Nguyen, “Selecting Optimal Context Sentences for Event-Event Relation Extraction,†Proc. ... AAAI Conf. Artif. Intell., vol. 36, no. 10, pp. 11058–11066, 2022, doi: 10.1609/aaai.v36i10.21354. [19] R. Duan, P. Guo, and D. Zhang, “Multi-Grained Ranking Network for Event Extraction,†J. Phys. Conf. Ser., vol. 2347, no. 1, p. 12023, Sep. 2022, doi: 10.1088/1742-6596/2347/1/012023. [20] R. Han, Y. Zhou, and N. Peng, “Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction,†in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020, pp. 5717–5729, doi: 10.18653/v1/2020.emnlp-main.461. [21] Q. Li et al., “A Survey on Deep Learning Event Extraction: Approaches and Applications,†IEEE Trans. Neural Networks Learn. Syst., vol. 35, no. 5, pp. 6301–6321, May 2024, doi: 10.1109/TNNLS.2022.3213168. [22] C. Heitzinger and S. Woltran, “A Short Introduction to Artificial Intelligence: Methods, Success Stories, and Current Limitations,†2023, pp. 135–149, doi: 10.1007/978-3-031-45304-5_9. [23] A. Bendimerad, M. Plantevit, C. Robardet, and S. Amer-Yahia, “User-Driven Geolocated Event Detection in Social Media,†IEEE Trans. Knowl. Data Eng., vol. 33, no. 2, pp. 796–809, 2021, doi: 10.1109/TKDE.2019.2931340. [24] J. Vasilakes, P. Georgiadis, N. T. H. Nguyen, M. Miwa, and S. Ananiadou, “Contextualized medication event extraction with levitated markers,†J. Biomed. Inform., vol. 141, no. March, p. 104347, 2023, doi: 10.1016/j.jbi.2023.104347. [25] J. Liu, Y. Chen, K. Liu, W. Bi, and X. Liu, “Event Extraction as Machine Reading Comprehension,†in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020, pp. 1641–1651, doi: 10.18653/v1/2020.emnlp-main.128. [26] Z. Li, N. Cheng, and W. Song, “Research on Chinese Event Extraction Method Based on RoBERTa-WWM-CRF,†pp. 100-104, Aug. 2021, doi: 10.1109/icsess52187.2021.9522150. [27] Y. Chen, L. Kong, Y. Wang, and D. Kong, “Multi-Grained Attention Representation With ALBERT for Aspect-Level Sentiment Classification,†IEEE Access, vol. 9, pp. 106703–106713, 2021, doi: 10.1109/ACCESS.2021.3100299. [28] J. Li, R. Hu, K. Zhang, H. Liu, and Y. Ma, “DEERE: Document-Level Event Extraction as Relation Extraction,†Mob. Inf. Syst., vol. 2022, pp. 1–8, 2022, doi: 10.1155/2022/2742796. [29] A. Shreyas, G. Priyanka, Merlyn Pearl, and Shinde Swapnal, “Event Information Extraction From E-Mail And Updating Event In Calendar,†Int. J. Adv. Res. Innov. Ideas Educ., vol. 4, no. 3, pp. 1242-1247, 2018. [Online]. Available at: https://ijariie.com/AdminUploadPdf/ . [30] F. Li, G. Chen, and X. Liu, “An event-based automatic annotation method for datasets of interpersonal relation extraction,†Appl. Intell., vol. 53, no. 3, pp. 2629–2639, 2023, doi: 10.1007/s10489-022-03547-8. [31] Q. Wan, C. Wan, K. Xiao, R. Hu, D. Liu, and X. Liu, “CFERE: Multi-type Chinese financial event relation extraction,†Inf. Sci. (Ny)., vol. 630, no. January, pp. 119–134, 2023, doi: 10.1016/j.ins.2023.01.143. [32] Z. Yan and X. Tang, “Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph,†J. Syst. Sci. Syst. Eng., vol. 32, no. 2, pp. 206–221, 2023, doi: 10.1007/s11518-023-5561-0. [33] W. Liu et al., “Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction,†ArXiv, vol. 1, pp. 1-18, no. 2, 2023. [Online]. Available at: https://arxiv.org/abs/2405.01884. [34] A. Romadhony, D. H. Widyantoro, and A. Purwarianti, “Utilizing structured knowledge bases in open IE based event template extraction,†Appl. Intell., vol. 49, no. 1, pp. 206–219, 2019, doi: 10.1007/s10489-018-1269-0. [35] R. N. Devendra Kumar, K. Srihari, C. Arvind, and W. Viriyasitavat, “Biomedical event extraction on input text corpora using combination technique based capsule network,†SÄdhanÄ, vol. 47, no. 4, p. 198, Sep. 2022, doi: 10.1007/s12046-022-01978-0. [36] G. Li, P. Wang, J. Xie, R. Cui, and Z. Deng, “FEED: A Chinese Financial Event Extraction Dataset Constructed by Distant Supervision,†in Proceedings of the 10th International Joint Conference on Knowledge Graphs, Dec. 2021, pp. 45–53, doi: 10.1145/3502223.3502229. [37] B. Portelli, D. Passabì, E. Lenzi, G. Serra, E. Santus, and E. Chersoni, “Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies,†2022, pp. 87–99, doi: 10.1007/978-3-030-93080-6_8. [38] H.-Y. Yu and M.-H. Kim, “Automatic Event Extraction method for Analyzing Text Narrative Structure,†in 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), Jan. 2021, pp. 1–4, doi: 10.1109/IMCOM51814.2021.9377386. [39] P. Purnawansyah et al., “Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning,†J. Appl. Data Sci., vol. 5, no. 4, pp. 1597–1613, Dec. 2024, doi: 10.47738/jads.v5i4.333. [40] A. P. Wibawa et al., “Decoding and preserving Indonesia’s iconic Keris via A CNN-based classification,†Telemat. Informatics Reports, vol. 13, p. 100120, Mar. 2024, doi: 10.1016/J.TELER.2024.100120.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Erna Daniati, Aji Prasetya Wibawa, Wahyu Sakti Gunawan Irianto

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
Science in Information Technology Letters
ISSN 2722-4139
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
W : http://pubs2.ascee.org/index.php/sitech
E : sitech@ascee.org, andri@ascee.org, andri.pranolo.id@ieee.org
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0