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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Pendidikan Sains Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIV : International Journal on Informatics Visualization VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro PEMBELAJAR: Jurnal Ilmu Pendidikan, Keguruan, dan Pembelajaran Pendas : Jurnah Ilmiah Pendidikan Dasar ILKOM Jurnal Ilmiah SENTIA 2016 Buletin Ilmiah Sarjana Teknik Elektro Letters in Information Technology Education (LITE) Jurnal Graha Pengabdian Ideguru: Jurnal Karya Ilmiah Guru Science in Information Technology Letters International Journal of Engineering, Science and Information Technology Bulletin of Community Engagement Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Integrasi dan Harmoni Inovatif Ilmu-ilmu Sosial Jurnal Inovasi Teknologi dan Edukasi Teknik ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat
Wahyu Sakti Gunawan Irianto
Universitas Negeri Malang
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Journal : JOIV : International Journal on Informatics Visualization

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Few-Shot-BERT-RNN Narrative Structure Analysis for Andersen's Stories Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Hernandez, Leonel
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3932

Abstract

Event Extraction (EE) is a pivotal task for NLP, where important events in the narrative text need to be detected and recognized. We present an alternative method for extracting events from Hans Christian Andersen's fairy tales, utilizing Few-Shot Learning with BERT (Bidirectional Encoder Representations from Transformers) and RNN (Recurrent Neural Network) in this paper. We selected Andersen's fairy tales because they are characterized by rich narratives and symbolic language, which also often prevents automatic event extraction. To reduce reliance on labeled samples, we utilize the Few-Shot Learning method, which enables the model to learn from a small number of labeled event examples trivially. The BERT model is used to generate deep representations by modeling the context between words and sentences. RNN is essential to capture the sequence of events in the story, which determines the structure of the narrative. The findings demonstrate that the proposed framework significantly improves event extraction, with high values of evaluation metrics such as in accuracy, precision, recall, and F1-score. The proposed method is also effective in extracting non-explicit events while keeping the narrative context. Despite the challenges posed by metaphorical language and subjective events, this work demonstrates that Few-Shot Learning, BERT, and RNNs offer a promising solution to the task of event extraction from complex narratives.
Attention-Enhanced Convolutional Neural Network for Context Extraction in Andersen's Fairy Tales Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Nafalski, Andrew
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4056

Abstract

Event extraction in classic literature and fairy tales remains highly challenging due to their non-linear plot structures, archaic linguistic expressions, and intricate character interactions, while advances in modern NLP still show limitations in capturing subtle narrative cues in historical texts. This study aims to address these gaps by developing an event extraction model tailored to the narrative characteristics of Hans Christian Andersen’s fairy tales. We propose a BERT-enhanced Context-aware Convolutional Neural Network (CNN) that integrates an attention mechanism to overcome the limited contextual range of traditional CNNs. The model leverages BERT’s contextual embeddings enriched with an attention layer to detect event triggers, character relations, and narrative transitions across nonlinear storylines. A hybrid dataset was constructed through system-generated annotations refined via manual verification and combined with AN/an cartoon-based representations for model training and final testing. Experimental results show that the proposed model surpasses both the CNN-only baseline and a rule-based approach, achieving precision of 0.92, recall of 0.89, F1-score of 0.90, and accuracy of 0.91, outperforming the CNN baseline (0.85/0.82/0.83/0.84) and rule-based system (0.78/0.75/0.76/0.77). These findings highlight the effectiveness of context-aware representations for processing literary narratives and demonstrate interdisciplinary relevance to digital humanities and AI-based storytelling, with future extensions envisioned for multilingual settings and genre-specific adaptations.
Co-Authors Abbar, Habib Muhammad Abdul Hadi, Afif Agro Lukman Putra Aguwin Ardi Pranata Ahmad Fuadi Ahsan, Muhammad Zamani Aji Prasetya Wibawa Al-Jabbar, Habib Muhammad Amrullah, Muhammad Shalahuddin Ananda Putri Syaviri Anik Nur Handayani Anusua Ghosh, Anusua Artina Tri Wistiawati Ashshiddiqi, Dimas Jundan Asmoro, Achmad Shoddiq Bayu Budi Wibowotomo Damanhuri, Nor Salwa Didik Dwi Prasetya Dila Umnia Soraya Dolly Indra Eka Rahayu Setyaningsih Erna Daniati Evania Kurniawati Fahreza, M. Dimas Aviv Falah, Moh. Zainul Fitri, Anisa Hilya Hakkun Elmunsyah Haqiqi, Ahmad Faiz Risvan Hasriani Hermansyah Heru Wahyu Herwanto I Made Wirawan Isyatul Karimah Joumil Aidil Saifuddin Khamdan, Candra Wahyu Nur Kirana, Karika Candra Krisnawan, Shandy Leonel Hernandez, Leonel lilis nurhayati M. Rodhi Faiz, M. Rodhi Milenia, Herpri Mohammad Husein An Nabawi Muhammad Afnan Habibi Muhammad Jauharul Fuady Mungalim, Rifqy Nafalski, Andrew Nugroho, Andhik Catur Nugroho, Bagas Putra, Adhi Pramana Estiawanda Rina Dewi Indahsari Rizka Afdalia Rosa Andrie Asmara Saifuddin, Farikh Saputra, Ismed Eko Hadi Sari, Gilang Rafiqa Setiadi Cahyono Putro Setyaningsih, Eka Rahayu Siti Sendari Slamet Wibawanto Sujito Sujito Syaad Patmanthara Syah, Abdullah Iskandar Triyanna Widiyaningtyas Ulum, Khoirul Utomo Pujianto Veithzal Rivai Zainal Wardhana, Nyoman Dedi Kusuma Widayanti, Erma Wistiawati, Artina Tri Yosi Kristian Yuni Rahmawati