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Slow emerging topic detection is a task between event detection, where weaggregate behaviors of different words on short period of time, and languageevolution, where we monitor their long term evolution. In this work, we tacklethe problem of early detection of slowly emerging new topics. To this end, wegather evidence of weak signals at the word level. We propose to monitor thebehavior of words representation in an embedding space and use one of itsgeometrical properties to characterize the emergence of topics. As evaluationis typically hard for this kind of task, we present a framework forquantitative evaluation. We show positive results that outperformstate-of-the-art methods on two public datasets of press and scientificarticles.

Monitoring geometrical properties of word embeddings for detecting the emergence of new topics