用NLP从故事中自动生成角色关系演变图谱
MARCUS: An Event-Centric NLP Pipeline that generates Character Arcs from Narratives
- 基于事件与人物关系提取角色动态演化路径
- 在《哈利·波特》和《指环王》中生成可可视化的角色弧线
- 适合文学分析、叙事结构研究的计算工具
角色弧是文学研究中理解人物成长、识别类型模板及发现叙事共性的核心理论工具。本文提出一个新任务：从叙事文本中自动构建以事件为中心、基于关系的角色弧。通过定量表征角色弧，使抽象概念具象化，为后续应用铺路。我们提出MARCUS（Modelling Arcs for Understanding Stories）——一种NLP流水线，能提取事件、角色、隐含情绪与情感极性，并追踪人物间关系随叙事发展而变化的过程，最终生成图形化角色弧。我们在《哈利·波特》和《指环王》两部长篇奇幻系列上生成角色弧，评估了方法有效性，指出当前挑战，展望应用场景与未来方向。
原文摘要 · Abstract (English)
Character arcs are important theoretical devices employed in literary studies to understand character journeys, identify tropes across literary genres, and establish similarities between narratives. This work addresses the novel task of computationally generating event-centric, relation-based character arcs from narratives. Providing a quantitative representation for arcs brings tangibility to a theoretical concept and paves the way for subsequent applications. We present MARCUS (Modelling Arcs for Understanding Stories), an NLP pipeline that extracts events, participant characters, implied emotion, and sentiment to model inter-character relations. MARCUS tracks and aggregates these relations across the narrative to generate character arcs as graphical plots. We generate character arcs from two extended fantasy series, Harry Potter and Lord of the Rings. We evaluate our approach before outlining existing challenges, suggesting applications of our pipeline, and discussing future work.
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