@InProceedings{jain-kasiviswanathan-huang:2016:WNUT,
  author    = {Jain, Ajit  and  Kasiviswanathan, Girish  and  Huang, Ruihong},
  title     = {Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {70--77},
  abstract  = {Accurate event detection in social media is very challenging because user
	generated contents are extremely noisy and sparse in content. Event indicators
	are generally words or phrases that act as a trigger that help us understand
	the semantics of the context they occur in. We present a weakly supervised
	approach that relies on  using a single strong event indicator phrase as a seed
	to acquire a variety of additional event cues. We propose to leverage various
	types of implicit event indicators, such as props, actors and precursor events,
	to achieve precise event detection. We experimented with civil
	unrest events and show that the automatically learnt event indicators are
	effective in identifying specific types of events.},
  url       = {http://aclweb.org/anthology/W16-3911}
}