@ARTICLE{10.21494/ISTE.OP.2024.1139, 

TITLE={A study of the influence of textual representation on event detection in data streams}, 
  
AUTHOR={Elliot MAÎTRE
, Max CHEVALIER
, Bernard DOUSSET
, Jean-Philippe GITTO
, Olivier TESTE, }, 
	
JOURNAL={Open Journal in Information Systems Engineering}, 
	
VOLUME={4}, 

NUMBER={Special Issue}, 
	
YEAR={2024}, 

URL={http://openscience.fr/A-study-of-the-influence-of-textual-representation-on-event-detection-in-data}, 
	
DOI={10.21494/ISTE.OP.2024.1139}, 
	
ISSN={2634-1468}, 
	
ABSTRACT={Detection of real-world events using online data sources is a trending topic in the information retrieval domain. Multiple data sources are potentially of interest and some of them are data streams. There are multiple data sources that are potentially interesting, and some of them are textual data streams, structured or unstructured. We propose to analyse the problem of event detection from text data stream and to focus particularly on the importance of the representation of the textual data. To do so, we compare multiple approaches in different contexts: supervised and unsupervised. We focus on the performances of Transformer-based architectures for event detection on short text documents, and we conclude that, contrary to previous studies, these architectures can be competitive compared to classical methods.}}