Research·arXiv cs.CL·6d ago

An Information-Geometric Justification for Composite Coherence in Event-Based Narrative Extraction

Researchers have grounded a widely-used narrative extraction metric in information geometry, proving that the composite coherence function C=sqrt(A*T) decomposes additively across angular and topical dimensions on a product manifold. This work bridges the gap between operational heuristics and principled mathematical foundations, offering NLP practitioners both theoretical justification for existing choices and a framework for designing future coherence measures. The result matters for event extraction pipelines that power knowledge graphs and temporal reasoning in language models.

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Explainer

The paper doesn't propose a new coherence metric; it proves why an old one works. The insight is that composite coherence decomposes cleanly on a product manifold, which means practitioners can now reason about which dimension (angular vs. topical) is failing in their extraction pipelines rather than treating coherence as a black box.

This connects directly to the distillation work on multilingual relation extraction (DistilledGemma, June 28) and the mechanistic data attribution paper (Symbolic Mechanistic Data Attribution, same date). Both those efforts depend on event and relation extraction pipelines that use coherence scoring to rank candidate outputs. Having a principled geometric foundation for coherence means future work can diagnose why extraction fails at the manifold level rather than empirically tuning weights. The Riemannian framing also echoes BrainRiem's approach to respecting geometric structure in domain adaptation, though applied here to narrative geometry rather than brain connectivity.

If a major NLP framework (Hugging Face, AllenNLP, or similar) ships a coherence module that explicitly exposes angular and topical dimensions as separate tuning parameters within the next 12 months, that signals practitioners are actually using this decomposition. If not, the result remains a theoretical justification without operational adoption.

Coverage we drew on

DistilledGemma: Balanced Efficiency-Accuracy for Person-Place Relation Extraction from Multilingual Historical Articles · arXiv cs.CL

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MentionsJensen-Shannon distance · Riemannian metric · event-based narrative extraction · graph-based NLP

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