@InProceedings{zhou-EtAl:2017:I17-4,
  author    = {Zhou, Xin  and  Wang, Jian  and  Xie, Xu  and  Sun, Changlong  and  Si, Luo},
  title     = {Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis 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     = {100--104},
  abstract  = {This paper introduces Team Alibaba's systems participating IJCNLP 2017 shared
	task No. 2 Dimensional Sentiment Analysis for Chinese Phrases (DSAP). The
	systems mainly utilize a multi-layer neural networks, with multiple features
	input such as word embedding, part-of-speech-tagging (POST), word clustering,
	prefix type, character embedding, cross sentiment input, and AdaBoost method
	for model training. For word level task our best run achieved MAE 0.545 (ranked
	2nd), PCC 0.892 (ranked 2nd) in valence prediction and MAE 0.857 (ranked 1st),
	PCC 0.678 (ranked 2nd) in arousal prediction. For average performance of word
	and phrase task we achieved MAE 0.5355 (ranked 3rd), PCC 0.8965 (ranked 3rd) in
	valence prediction and MAE 0.661 (ranked 3rd), PCC 0.766 (ranked 2nd) in
	arousal prediction. In the final our submitted system achieved 2nd in mean
	rank.},
  url       = {http://www.aclweb.org/anthology/I17-4016}
}