@InProceedings{stanovsky-EtAl:2017:Short,
  author    = {Stanovsky, Gabriel  and  Eckle-Kohler, Judith  and  Puzikov, Yevgeniy  and  Dagan, Ido  and  Gurevych, Iryna},
  title     = {Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {352--357},
  abstract  = {Previous models for the assessment of commitment towards a predicate in a
	sentence (also known as factuality prediction) were trained and tested against
	a specific annotated dataset, subsequently limiting the generality of their
	results. In this work we propose an intuitive method for mapping three
	previously annotated corpora onto a single factuality scale, thereby enabling
	models to be tested across these corpora. In addition, we design a novel model
	for factuality prediction by first extending a previous rule-based factuality
	prediction system and applying it over an abstraction of dependency trees, and
	then using the output of this system in a supervised classifier. We show that
	this model outperforms previous methods on all three datasets. We make both the
	unified factuality corpus and our new model publicly available.},
  url       = {http://aclweb.org/anthology/P17-2056}
}