@InProceedings{wang-wang-zhang:2017:I17-4,
  author    = {Wang, Nan  and  Wang, Jin  and  Zhang, Xuejie},
  title     = {YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {174--179},
  abstract  = {This paper describes our submission to IJCNLP 2017 shared task 4, for
	predicting the tags of unseen customer feedback sentences, such as comments,
	complaints, bugs, requests, and meaningless and undetermined statements. With
	the use of a neural network, a large number of deep learning methods have been
	developed, which perform very well on text classification. Our ensemble
	classification model is based on a bi-directional gated recurrent unit and an
	attention mechanism which shows a 3.8\% improvement in classification accuracy.
	To enhance the model performance, we also compared it with several
	word-embedding models. The comparative results show that a combination of both
	word2vec and GloVe achieves the best performance.},
  url       = {http://www.aclweb.org/anthology/I17-4029}
}