@InProceedings{chen-EtAl:2017:Long1,
  author    = {Chen, Yubo  and  Liu, Shulin  and  Zhang, Xiang  and  Liu, Kang  and  Zhao, Jun},
  title     = {Automatically Labeled Data Generation for Large Scale Event Extraction},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {409--419},
  abstract  = {Modern models of event extraction for tasks like ACE are based on supervised
	learning of events from small hand-labeled data. However, hand-labeled training
	data is expensive to produce, in low coverage of event types, and limited in
	size, which makes supervised methods hard to extract large scale of events for
	knowledge base population. To solve the data labeling problem, we propose to
	automatically label training data for event extraction via world knowledge and
	linguistic knowledge, which can detect key arguments and trigger words for each
	event type and employ them to label events in texts automatically. The
	experimental results show that the quality of our large scale automatically
	labeled data is competitive with elaborately human-labeled data. And our
	automatically labeled data can incorporate with human-labeled data, then
	improve the performance of models learned from these data.},
  url       = {http://aclweb.org/anthology/P17-1038}
}