@InProceedings{li-EtAl:2016:COLING8,
  author    = {Li, Shoushan  and  Xu, Jian  and  Zhang, Dong  and  Zhou, Guodong},
  title     = {Two-View Label Propagation to Semi-supervised Reader Emotion Classification},
  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     = {2647--2655},
  abstract  = {In the literature, various supervised learning approaches have been adopted to
	address the task of reader emotion classification. However, the classification
	performance greatly suffers when the size of the labeled data is limited. In
	this paper, we propose a two-view label propagation approach to semi-supervised
	reader emotion classification by exploiting two views, namely source text and
	response text in a label propagation algorithm. Specifically, our approach
	depends on two word-document bipartite graphs to model the relationship among
	the samples in the two views respectively. Besides, the two bipartite graphs
	are integrated by linking each source text sample with its corresponding
	response text sample via a length-sensitive transition probability. In this
	way, our two-view label propagation approach to semi-supervised reader emotion
	classification largely alleviates the reliance on the strong sufficiency and
	independence assumptions of the two views, as required in co-training.
	Empirical evaluation demonstrates the effectiveness of our two-view label
	propagation approach to semi-supervised reader emotion classification.},
  url       = {http://aclweb.org/anthology/C16-1249}
}