@InProceedings{kang-EtAl:2017:EMNLP2017,
  author    = {Kang, Dongyeop  and  Gangal, Varun  and  Lu, Ang  and  Chen, Zheng  and  Hovy, Eduard},
  title     = {Detecting and Explaining Causes From Text For a Time Series Event},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2758--2767},
  abstract  = {Explaining underlying causes or effects about events is a challenging but
	valuable task.
	We define a novel problem of generating explanations of a time series event by
	(1) searching cause and effect relationships of the time series with textual
	data and (2) constructing a connecting chain between them to generate an
	explanation.
	To detect causal features from text, we propose a novel method based on the
	Granger causality of time series between features extracted from text such as
	N-grams, topics, sentiments, and their composition.
	The generation of the sequence of causal entities requires a commonsense
	causative knowledge base with efficient reasoning. 
	To ensure good interpretability and appropriate lexical usage we combine
	symbolic and neural representations, using a neural reasoning algorithm trained
	on commonsense causal tuples to predict the next cause step.
	Our quantitative and human analysis show empirical evidence that our method
	successfully extracts meaningful causality relationships between time series
	with textual features and generates appropriate explanation between them.},
  url       = {https://www.aclweb.org/anthology/D17-1292}
}