Dialogue-Based Multi-Dimensional Relationship Extraction from Novels
This addresses the problem of extracting complex and implicit character relationships from novels for applications in knowledge graphs and literary analysis, though it is incremental with a focus on a specific domain.
The study tackled relation extraction from novels by proposing a Large Language Model-based method with dialogue data and contextual learning, resulting in outperformance over traditional baselines in metrics and enabling automated character relationship network construction.
Relation extraction is a crucial task in natural language processing, with broad applications in knowledge graph construction and literary analysis. However, the complex context and implicit expressions in novel texts pose significant challenges for automatic character relationship extraction. This study focuses on relation extraction in the novel domain and proposes a method based on Large Language Models (LLMs). By incorporating relationship dimension separation, dialogue data construction, and contextual learning strategies, the proposed method enhances extraction performance. Leveraging dialogue structure information, it improves the model's ability to understand implicit relationships and demonstrates strong adaptability in complex contexts. Additionally, we construct a high-quality Chinese novel relation extraction dataset to address the lack of labeled resources and support future research. Experimental results show that our method outperforms traditional baselines across multiple evaluation metrics and successfully facilitates the automated construction of character relationship networks in novels.