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This research advances natural language processing by adapting pointwise mutual information to highlight peripheral narrative structures that traditional models overlook. By weighting edges with PMI, the method effectively surfaces minor characters and sub-plots within complex texts like *The Lord of the Rings*, offering a novel lens for exploring fine-grained story details. While sensitive to weak signals, this approach provides valuable insights into the long tail of entity associations in computational narrative analysis.
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Molecules of a Story: Community Detection in PMI-weighted Narrative Networks

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arXiv:2610.06600v1 Announce Type: new Abstract: 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.

Abstract: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.

Computation and Language (cs.CL)

arXiv:2610.06600 [cs.CL]

(or arXiv:2610.06600v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.06600 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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