@InProceedings{zhan-EtAl:2017:Short,
  author    = {Zhan, Xueying  and  Wang, Yaowei  and  Rao, Yanghui  and  Xie, Haoran  and  Li, Qing  and  Wang, Fu Lee  and  Wong, Tak-Lam},
  title     = {A Network Framework for Noisy Label Aggregation in Social Media},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {484--490},
  abstract  = {This paper focuses on the task of noisy label aggregation in social media,
	where users with different social or culture backgrounds may annotate invalid
	or malicious tags for documents. To aggregate noisy labels at a small cost, a
	network framework is proposed by calculating the matching degree of a
	document's topics and the annotators' meta-data. Unlike using the
	back-propagation algorithm, a probabilistic inference approach is adopted to
	estimate network parameters. Finally, a new simulation method is designed for
	validating the effectiveness of the proposed framework in aggregating noisy
	labels.},
  url       = {http://aclweb.org/anthology/P17-2077}
}