用大模型从小说对话中自动提取人物关系，效果优于传统方法。
Dialogue-Based Multi-Dimensional Relationship Extraction from Novels
- 基于大模型，分离关系维度并利用对话结构增强理解
- 在自建中文小说数据集上，各项指标均超越传统基线
- 适合文学分析、知识图谱构建等需要深层关系挖掘的场景
关系抽取是自然语言处理中的关键任务，广泛应用于知识图谱构建和文学分析。然而，小说文本中复杂的上下文和隐含表达给人物关系的自动抽取带来巨大挑战。本研究聚焦小说领域的关系抽取，提出一种基于大语言模型的方法。通过引入关系维度分离、对话数据构建和上下文学习策略，该方法提升了抽取性能。利用对话结构信息，增强了模型对隐含关系的理解能力，并在复杂语境下表现出强适应性。此外，我们构建了一个高质量的中文小说关系抽取数据集，以弥补标注资源匮乏的问题，推动后续研究。实验结果表明，该方法在多个评估指标上均优于传统基线，成功实现了小说人物关系网络的自动化构建。
原文摘要 · Abstract (English)
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.
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