@InProceedings{gencheva-EtAl:2017:RANLP,
  author    = {Gencheva, Pepa  and  Nakov, Preslav  and  M\`{a}rquez, Llu\'{i}s  and  Barr\'{o}n-Cede\~{n}o, Alberto  and  Koychev, Ivan},
  title     = {A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates},
  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     = {267--276},
  abstract  = {In the context of investigative journalism, we address the problem of
	automatically identifying which claims in a given document are most worthy and
	should be prioritized for fact-checking. Despite its importance, this is a
	relatively understudied problem. Thus, we create a new corpus of political
	debates, containing statements that have been fact-checked by nine reputable
	sources, and we train machine learning models to predict which claims should be
	prioritized for fact-checking, i.e., we model the problem as a ranking task. 
	Unlike previous work, which has looked primarily at sentences in isolation, in
	this paper we focus on a rich input representation modeling the context:
	relationship between the target statement and the larger context of the debate,
	interaction between the opponents, and reaction by the moderator and by the
	public. Our experiments show state-of-the-art results, outperforming a strong
	rivaling system by a margin, while also confirming the importance of the
	contextual information.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_037}
}