@InProceedings{lee-EtAl:2017:I17-4,
  author    = {Lee, Yen-Hsuan  and  Yeh, Han-Yun  and  Wang, Yih-Ru  and  Liao, Yuan-Fu},
  title     = {NCTU-NTUT at IJCNLP-2017 Task 2: Deep Phrase Embedding using bi-LSTMs for Valence-Arousal Ratings Prediction of Chinese Phrases},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {124--129},
  abstract  = {In this paper, a deep phrase embedding approach using bi-directional long
	short-term memory (Bi-LSTM) is proposed to predict the valence-arousal ratings
	of Chinese words and phrases. It adopts a Chinese word segmentation frontend, a
	local order-aware word, a global phrase embedding representations and a deep
	regression neural network (DRNN) model. The performance of the proposed method
	was benchmarked by the IJCNLP 2017 shared task 2. According the official
	evaluation results, our best system achieved mean rank 6.5 among all 24
	submissions.},
  url       = {http://www.aclweb.org/anthology/I17-4020}
}