Attention-Enhanced Convolutional Neural Network for Context Extraction in Andersen's Fairy Tales - Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Nafalski, Andrew (Society of Visual Informatics, 2025)
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.