@InProceedings{huang-yang-chen:2016:COLING,
  author    = {Huang, Hen-Hsen  and  Yang, Chang-Rui  and  Chen, Hsin-Hsi},
  title     = {Chinese Tense Labelling and Causal Analysis},
  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     = {2227--2237},
  abstract  = {This paper explores the role of tense information in Chinese causal analysis.
	Both tasks of causal type classification and causal directionality
	identification are experimented to show the significant improvement gained from
	tense features. To automatically extract the tense features, a Chinese tense
	predictor is proposed. Based on large amount of parallel data, our
	semi-supervised approach improves the dependency-based convolutional neural
	network (DCNN) models for Chinese tense labelling and thus the causal analysis.},
  url       = {http://aclweb.org/anthology/C16-1210}
}