1 citations · 1 across the 7 of their papers we have counts for
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Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space
Evangelia Zve, Gauvain Bourgne, Jean-Gabriel Ganascia
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 scattere…
From Noise to Signal: When Outliers Seed New Topics
Evangelia Zve, Gauvain Bourgne, Benjamin Icard +1
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-doc…
From Outliers to Topics in Language Models: Anticipating Trends in News Corpora
Evangelia Zve, Benjamin Icard, Alice Breton +3
This paper examines how outliers, often dismissed as noise in topic modeling, can act as weak signals of emerging topics in dynamic news corpora. Using vector embeddings from state…