@InProceedings{ponti-korhonen:2017:LSDSem,
  author    = {Ponti, Edoardo Maria  and  Korhonen, Anna},
  title     = {Event-Related Features in Feedforward Neural Networks Contribute to Identifying Causal Relations in Discourse},
  booktitle = {Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {25--30},
  abstract  = {Causal relations play a key role in information extraction and reasoning. Most
	of the times, their expression is ambiguous or implicit, i.e. without signals
	in the text. This makes their identification challenging. We aim to improve
	their identification by implementing a Feedforward Neural Network with a novel
	set of features for this task. In particular, these are based on the position
	of event mentions and the semantics of events and participants. The resulting
	classifier outperforms strong baselines on two datasets (the Penn Discourse
	Treebank and the CSTNews corpus) annotated with different schemes and
	containing examples in two languages, English and Portuguese. This result
	demonstrates the importance of events for identifying discourse relations.},
  url       = {http://aclweb.org/anthology/W17-0903}
}