@InProceedings{bois-EtAl:2017:NLPmJ,
  author    = {Bois, R\'{e}mi  and  Gravier, Guillaume  and  Jamet, Eric  and  Morin, Emmanuel  and  S\'{e}billot, Pascale  and  Robert, Maxime},
  title     = {Language-based Construction of Explorable News Graphs for Journalists},
  booktitle = {Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {31--36},
  abstract  = {Faced with ever-growing news archives, media professionals are in need of
	advanced tools to explore the information surrounding specific events. 
	This problem is most commonly answered by browsing news datasets, going from
	article to article and viewing unaltered original content.
	In this article, we introduce an efficient way to generate links between news
	items, allowing such browsing through an easily explorable graph, and enrich
	this graph by automatically typing links in order to inform the user on the
	nature of the relation between two news pieces.
	User evaluations are conducted on real world data with journalists in order to
	assess for the interest of both the graph representation and link typing in a
	press reviewing task, showing the system to be of significant help for their
	work.},
  url       = {http://www.aclweb.org/anthology/W17-4206}
}