@InProceedings{edouard-EtAl:2017:RANLP3,
  author    = {Edouard, Amosse  and  Cabrio, Elena  and  Tonelli, Sara  and  LE-THANH, Nhan},
  title     = {Graph-based Event Extraction from Twitter},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {222--230},
  abstract  = {Detecting which tweets describe a specific event and clustering them is one
	of the main challenging tasks related to Social Media currently addressed in
	the NLP community. Existing approaches have mainly focused on detecting spikes
	in clusters around specific keywords or Named Entities (NE). However, one of
	the main drawbacks of such approaches is the difficulty in understanding when
	the same keywords describe different events. In this paper, we propose a novel
	approach that exploits NE mentions in tweets and their entity context to create
	a temporal event graph. Then, using simple graph theory techniques and a
	PageRank-like algorithm, we process the event graphs to detect clusters of
	tweets describing the same events. Experiments on two gold standard datasets
	show that our approach achieves state-of-the-art results both in terms of
	evaluation performances and the quality of the detected events.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_031}
}