Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space
Some documents that embedding-based topic models initially classify as noise later become founding members of emerging topics. At publication time, however, they appear as scattered points in embedding space and are difficult to distinguish from ordinary noise without the benefit of hindsight. We study whether such anticipatory outliers can be predicted prospectively, using only information available when a document first appears. We derive labels from the subsequent trajectories of outlier documents, distinguishing those that anticipate new topics from those that reinforce existing topics or remain isolated, and estimate label confidence through agreement across multiple embedding models...
| Authors | Evangelia Zve, Gauvain Bourgne, Jean-Gabriel Ganascia |
|---|---|
| Primary category | cs.CL |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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