@InProceedings{zhu-EtAl:2016:COLING,
  author    = {Zhu, Suyang  and  Li, Shoushan  and  Chen, Ying  and  Zhou, Guodong},
  title     = {Corpus Fusion for 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     = {3287--3297},
  abstract  = {Machine learning-based methods have obtained great progress on emotion
	classification. However, in most previous studies, the models are learned based
	on a single corpus which often suffers from insufficient labeled data. In this
	paper, we propose a corpus fusion approach to address emotion classification
	across two corpora which use different emotion taxonomies. The objective of
	this approach is to utilize the annotated data from one corpus to help the
	emotion classification on another corpus. An Integer Linear Programming (ILP)
	optimization is proposed to refine the classification results. Empirical
	studies show the effectiveness of the proposed approach to corpus fusion for
	emotion classification.},
  url       = {http://aclweb.org/anthology/C16-1310}
}