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All Journal Nusanatara of Engineering JURNAL PENGABDIAN KEPADA MASYARAKAT PROCEEDING IC-ITECHS 2014 JOIV : International Journal on Informatics Visualization MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Antivirus : Jurnal Ilmiah Teknik Informatika Jambura Journal of Electrical and Electronics Engineering Jurnal Tecnoscienza JATI (Jurnal Mahasiswa Teknik Informatika) Respati JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Computer Networks, Architecture and High Performance Computing Science in Information Technology Letters Journal of Soft Computing Exploration International Journal of Engineering, Science and Information Technology Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Archive: Jurnal Pengabdian Kepada Masyarakat Jurnal Borneo Informatika dan Teknik Komputer (JBIT) The Indonesian Journal of Computer Science Research Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Journal of Entrepreneurial and Business Diversity
Erna Daniati
Universitas Nusantara PGRI Kediri
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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 Abadi, Ahmad Fajar Abu Tholib Achmad, Ridho Afrizal Ahmad Bayu P Agustama, Andri Tri Aidina Ristyawan Aidina Ristyawan, Aidina Aji Prasetya Wibawa Akmal Hisyam Pradhana Alamsyah, M Alfianto Aliyyah Fitri Nur'aini Alja, Farhan Maulana Amarya, Theo Krisna Amelia Nur Fadhila Andy G, Asye Candra Angga Wibisono Anita Sari Wardani ANUARIDLO, Mochamad Aldi Yusuf Anusua Ghosh, Anusua Ardiansyah , Bima Ardyansyah, Fikri Arie Nugroho, Arie Arie, Theo Yan Arti Romansa, Shasya Aulia, Ewanda Herdika Septa Azzahra, Salsabila Dini Bachti, Achmad Syauqi Bastian Dwiki Prasetyo Christy Atika Sari Cintiana Adisti, Talita Cristina Juwita Agata Dewi, Candrika Arlita Diah Kurniawati, Virginia Dimas Abimanyu Dimas Regga Meydianto Ditto Syahrul S Dwi Hariani Dwi Harini DWI HARINI Dwi Harini Dzatama, Krisna Fahrizal Eko Hari Rachmawanto Ery Mintorini Fadhila, Amelia Nur Fadli Hidayat, M Noer Fadli Hidayat, M. Noer Farhan Gagat Retnanto Faruq, Umar Al Faruqziddan, Muhammad Fatayasya, Ikhfal Fauzi, Mohammad Ainun Naja Felmidi, Ferdian Ahmat Firmansyah, Achmad Ali Fitriono, Deri Hastari Utama Herdika Septa Aulia, Ewanda Hyperastuty, Agoes Santika Ilahi, Ferlita Putri Anugerah Intan Aprilia Rahman Irfa’udin, Muhammad Islami, Bifadhlillah Marsheila Jauhar, Moh. Iqbal Iqza Kamilatutsaniya, Nila Khalid, Muhammad Iqbal Leonel Hernandez, Leonel Lestari, Afifah Kurnia Lukman, Muhammad Abi Maemunah, Mei Maha Shelin Sahira Masruro, Ahlihi Moh Kusen Muhammad Fahmi Muhammad Fikri Pratama Muhammad Hafiz Yustiar Muhammad Imron Amrulloh Muhammad Najibulloh Muzaki Mustofa, Mohammad Annan Makruf Muzaki, Muhammad Reza Nafalski, Andrew Naufal Rizqulloh Ningrum, Dea Yuliana Ayu Nngrum, Dea Yuliana Ayu Nugroho , Arie Nugroho, Arie Nur Alamsyah, Nur Nurfajriana, Intan Melinda Pradhana, Akmal Hisyam Prayogi, Anindita Puspa Ayu Priyanto, Evania Putra, Regi Candra Purnama Putri Wahyuni, Hesti Putri, Fitria Dessela Putriani, Dewi Ratih Kumalasari Niswatin Resty Wulanningrum Rina Firliana Rini Indriati Rini Indriati Rino Adi Kurniawan Ristiyawan, Aidina Ristyawan , Aidina Rizki Wahyu Nugroho Rosyidah Jayanti Vijaya, Rosyidah Jayanti Sahira, Maha Shelin Sakin, Kharisma Santoso, Heru Teguh Sari Wardani, Anita Sasongko, Muhammad Zuhdi Setiawan, Fachruddin Ari Shofyana, Altha Inas Sri Ngudi Wahyuni, Sri Ngudi Suci Dewanti Sucipto Sucipto sugandhi sugandhi saputra Supri yono Supri Yono, Supri Syafa’at, Achmadhin Tristan Teguh Andriyanto Theo Krisna Amarya Thoyib Fernanda Nanda Utama, Hastari Varuq, M Nizar Bahri Al Wahiid, Hermawan Nur Wahyu Sakti Gunawan Irianto Wardani, Anita Sari Wardani, Saylendra Arga Wardhani, Aurel Fransisca Kusuma Wiranata, Hadi Wiwik Juniati Yongki Dyno Oka Satria Yulva Irfan Anas Yuszril Herdianzah Yuszril