故事分子：PMI加权叙事网络中的社区检测

中文摘要 AI 辅助

自动提取的叙事网络——以实体为节点、以实体间关系为边的图——已被证明通过显著实体及其连接揭示核心叙事结构是有用的（Tangherlini等人，2020；Labatut和Bost，2019）。但一个叙事不仅仅是这些其他一切围绕其旋转的核心结构。这项工作关注的是“其他一切”：简短的子情节、小规模的描述聚类，或次要角色之间的关联，这些在宏观层面往往被忽视。我们提出了一种方法来发掘这些边缘结构。它们涉及稀有实体，这些实体在文本中出现有限，被主导实体所掩盖，并在众多但稀有的实体的长尾中彼此迷失（Baayen，2001）。我们利用点互信息（PMI，Church和Hanks，1990）对稀有事件膨胀的已知倾向，将其弱点转化为优势，通过用PMI加权边来突出边缘实体配置。从所得网络中提取的社区是底层叙事元素的结构痕迹。我们在《指环王》上演示了该方法。从社区激活在文本中的集中或分散程度的度量中，出现了一种类型学，揭示边缘结构不仅仅形成单一类别：情节性段落、回响长距离连接和反复出现的线索各自呈现为不同的配置。该方法在概念上简单，并能浮现出在丰富性中丢失的细粒度叙事细节，尽管其对弱信号的有意放大带有固有的敏感性——最好将其理解为一种探索的透镜，而非稳健的提取流水线。

英文摘要

Automatically extracted narrative networks -- graphs with entities as nodes and their relations as edges -- have proven useful for revealing central narrative structures through salient entities and their connections (Tangherlini et al. 2020; Labatut and Bost 2019). But a narrative is more than those central structures that everything else revolves around. This work is concerned with the everything else: brief sub-plots, small clusters of descriptions, or associations between minor characters that go under the radar at the macro-level. We present an approach to unearth such peripheral structures. They involve rare entities with limited textual presence, overshadowed by dominant entities and lost among each other in the long tail of many but rare entities (Baayen 2001). We leverage the known tendency of pointwise mutual information (PMI, Church and Hanks 1990) to inflate for rare events, turning its weakness into a strength by weighting edges with PMI to foreground peripheral entity configurations. Communities extracted from the resulting network are structural traces of underlying narrative elements. We demonstrate the approach on The Lord of the Rings. From measures of how concentrated or dispersed a community's activations are across the text, a typology emerges that reveals that peripheral structures form more than a single class: episodic passages, echoing long-distance connections, and recurring threads each surface as distinct configurations. The approach is conceptually simple and surfaces fine-grained narrative details that are lost in abundance, though its deliberate amplification of weak signals comes with inherent sensitivity -- best understood as a lens for exploration rather than a robust extraction pipeline.

发表机构

• Aarhus University（奥胡斯大学）

机构由 AI 辅助整理，请以论文原文为准。