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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 st
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