@InProceedings{cambria-EtAl:2016:COLING,
  author    = {Cambria, Erik  and  Poria, Soujanya  and  Bajpai, Rajiv  and  Schuller, Bjoern},
  title     = {SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2666--2677},
  abstract  = {An important difference between traditional AI systems and human intelligence
	is the human ability to harness commonsense knowledge gleaned from a lifetime
	of learning and experience to make informed decisions. This allows humans to
	adapt easily to novel situations where AI fails catastrophically due to a lack
	of situation-specific rules and generalization capabilities. Commonsense
	knowledge also provides background information that enables humans to
	successfully operate in social situations where such knowledge is typically
	assumed. Since commonsense consists of information that humans take for
	granted, gathering it is an extremely difficult task. Previous versions of
	SenticNet were focused on collecting this kind of knowledge for sentiment
	analysis but they were heavily limited by their inability to generalize.
	SenticNet 4 overcomes such limitations by leveraging on conceptual primitives
	automatically generated by means of hierarchical clustering and dimensionality
	reduction.},
  url       = {http://aclweb.org/anthology/C16-1251}
}