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From Noise to Signal: When Outliers Seed New Topics

Summary

Dynamic topic models usually discard low-density documents as noise, though some of them may be early hints of topics that have not yet formed. The authors propose a document-level temporal taxonomy, inspired by Allen's interval algebra. It tracks three moments for each article: when it first appears, when its eventual topic is created, and when the article first joins that topic. Ordering these events gives seven mutually exclusive trajectory types. The key type is the anticipatory outlier (T OA), a document that shows up before the cluster it later enters.

To test the taxonomy, they built HydroNewsFr, a French corpus about the hydrogen economy with continuous daily coverage. Articles were embedded with eleven language models and clustered cumulatively in daily windows using density-based algorithms (HDBSCAN, OPTICS) after UMAP reduction. Clusters were then aligned across time and each document was labeled retrospectively.

Agreement across models singles out a small, high-consensus set of anticipatory outliers, which makes those labels more trustworthy. Qualitative case studies show these trajectories in concrete topic developments. The work links weak-signal detection with temporal topic modeling.

Key points

  • HydroNewsFr corpus: 1,616 deduplicated French articles covering 81 consecutive days (20 March–8 June 2025).
    • Sources were X posts containing hydrogène (1,533 posts, 726 unique articles) and the GNews library (891 articles).
    • Each document is a headline plus description, averaging about 280 characters.
  • Seven taxonomy cases:
    • T OA first and T OA late (anticipatory outliers)
    • T OD late (drift outlier that reinforces an existing topic)
    • T first and T late (non-outlier integrations)
    • O recent and O old (never integrated)
    • The cutoff θ delay separating O recent from O old is the 90th percentile of observed integration delays.
  • Embedding models: eleven models chosen via MTEB, including bge-m3, LaBSE, multilingual-e5-large, sentence-camembert-base, Solon-embeddings-large-0.1, mistral-embed, gemini and text-embedding-3-small.
  • Share of anticipatory outliers: at θ align = 0.30, T OA make up roughly 0.11–0.46 of all outlier documents, with averages near 0.27–0.33, across UMAP dimensionalities of 2D–40D.
  • Label stability: Fleiss' κ across UMAP dimensionalities has mean values of about 0.30–0.47, depending on setup and κ variant. These values barely change when θ align varies from 0.2 to 0.7.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/5c6zvq4nbjdq

@inproceedings{zve2026noise,
  title = {{From Noise to Signal: When Outliers Seed New Topics}},
  author = {Zve, Evangelia and Bourgne, Gauvain and Icard, Benjamin and Ganascia, Jean-Gabriel},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/5c6zvq4nbjdq},
  url = {https://doi.org/10.63317/5c6zvq4nbjdq}
}

References

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