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In this work, we evaluate the performance of recent text embeddings for theautomatic detection of events in a stream of tweets. We model this task as adynamic clustering problem.Our experiments are conducted on a publiclyavailable corpus of tweets in English and on a similar dataset in Frenchannotated by our team. We show that recent techniques based on deep neuralnetworks (ELMo, Universal Sentence Encoder, BERT, SBERT), although promising onmany applications, are not very suitable for this task. We also experiment withdifferent types of fine-tuning to improve these results on French data.Finally, we propose a detailed analysis of the results obtained, showing thesuperiority of tf-idf approaches for this task.

Repr\'esentations lexicales pour la d\'etection non supervis\'ee  d'\'ev\'enements dans un flux de tweets : \'etude sur des corpus fran\c{c}ais  et anglais