@InProceedings{karadzhov-EtAl:2017:RANLP2,
  author    = {Karadzhov, Georgi  and  Nakov, Preslav  and  M\`{a}rquez, Llu\'{i}s  and  Barr\'{o}n-Cede\~{n}o, Alberto  and  Koychev, Ivan},
  title     = {Fully Automated Fact Checking Using External Sources},
  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     = {344--353},
  abstract  = {Given the constantly growing proliferation of false claims online in recent
	years, there has been also a growing research interest in automatically
	distinguishing false rumors from factually true claims. Here, we propose a
	general-purpose framework for fully-automatic fact checking using external
	sources, tapping the potential of the entire Web as a knowledge source to
	confirm or reject a claim. Our framework uses a deep neural network with LSTM
	text encoding to combine semantic kernels with task-specific embeddings that
	encode a claim together with pieces of potentially relevant text fragments from
	the Web, taking the source reliability into account. The evaluation results
	show good performance on two different tasks and datasets: (i) rumor detection
	and (ii) fact checking of the answers to a question in community question
	answering forums.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_046}
}