aiqbench analysis
This research reframes outliers in dynamic topic modeling not as noise, but as early signals of emerging topics within evolving clusters. By leveraging document embeddings from multiple state-of-the-art language models, the study establishes a temporal taxonomy that distinguishes anticipatory documents from those reinforcing existing themes or remaining isolated. The findings demonstrate how weak-signal detection can be integrated with temporal clustering to improve confidence in identifying nascent discourse trajectories.
From Noise to Signal: When Outliers Seed New Topics
arXiv:2603.18358v2 Announce Type: replace Abstract: Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals of emerging topics. We introduce a temporal taxonomy of news-document trajectories that defines how documents relate to topic formation over time. It distinguishes anticipatory outliers, which precede the topics they later join, from documents that either reinforce existing topics or remain isolated. By capturing these trajectories, the taxonomy links weak-signal detection with temporal topic modeling and clarifies how individual articles anticipate, initiate, or drift within evolving clusters. We implement it in a cumulative clustering setting using document embeddings from eleven state-of-the-art language models and evaluate it retrospectively on HydroNewsFr, a French news corpus on the hydrogen economy. Inter-model agreement reveals a small, high-consensus subset of anticipatory outliers, increasing confidence in these labels. Qualitative case studies further illustrate these trajectories through concrete topic developments.
Abstract:Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals of emerging topics. We introduce a temporal taxonomy of news-document trajectories that defines how documents relate to topic formation over time. It distinguishes anticipatory outliers, which precede the topics they later join, from documents that either reinforce existing topics or remain isolated. By capturing these trajectories, the taxonomy links weak-signal detection with temporal topic modeling and clarifies how individual articles anticipate, initiate, or drift within evolving clusters. We implement it in a cumulative clustering setting using document embeddings from eleven state-of-the-art language models and evaluate it retrospectively on HydroNewsFr, a French news corpus on the hydrogen economy. Inter-model agreement reveals a small, high-consensus subset of anticipatory outliers, increasing confidence in these labels. Qualitative case studies further illustrate these trajectories through concrete topic developments.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2603.18358 [cs.CL]
(or arXiv:2603.18358v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2603.18358 Focus to learn more arXiv-issued DOI via DataCite
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