Abstract
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.
Abstract (translated)
关系抽取是自然语言处理中的一个关键任务，在知识图谱构建和文学分析等领域有着广泛的应用。然而，小说文本中复杂的语境和隐含表达给自动人物关系提取带来了巨大挑战。本研究专注于小说领域的关系抽取，并提出了一种基于大规模语言模型（LLMs）的方法。通过引入关系维度分离、对话数据构造以及上下文学习策略，该方法提升了抽取性能。利用对话结构信息，增强了模型理解隐含关系的能力，并展示了在复杂语境中的强大适应性。此外，我们构建了一个高质量的中文小说关系提取数据集，以应对标注资源不足的问题并支持未来的研究工作。实验结果表明，我们的方法在多个评估指标上均优于传统的基线方法，并成功实现了自动构建小说中人物关系网络的目标。
URL
https://arxiv.org/abs/2507.04852
