@InProceedings{yao-nettyam-huang:2017:RANLP,
  author    = {Yao, Wenlin  and  Nettyam, Saipravallika  and  Huang, Ruihong},
  title     = {A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {803--812},
  abstract  = {Capabilities of detecting temporal and causal relations between two events can
	benefit many applications. Most of existing temporal relation classifiers 
	were trained in a supervised  manner. Instead, we explore the observation
	that regular event pairs show a consistent temporal relation despite of their
	various contexts and these rich contexts can be used to train a contextual
	temporal relation classifier, which can further recognize new temporal relation
	contexts and identify new regular event pairs. We focus on detecting after and
	before temporal relations and design a weakly supervised learning approach that
	extracts thousands of regular event pairs and learns a contextual temporal
	relation classifier simultaneously. Evaluation shows that the acquired regular
	event pairs are of high quality and contain rich commonsense knowledge and
	domain specific knowledge. In addition, the weakly supervised  trained temporal
	relation  classifier achieves comparable performance with the state-of-the-art
	supervised systems.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_103}
}