@InProceedings{zhao-yang-xu:2017:SemEval,
  author    = {Zhao, Jingjing  and  Yang, Yan  and  Xu, Bing},
  title     = {MI\&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {689--693},
  abstract  = {A CNN method for sentiment classification task in Task 4A of SemEval 2017 is
	presented. To solve the problem of word2vec training word vector slowly, a
	method of training word vector by integrating word2vec and Convolutional Neural
	Network (CNN) is proposed. This training method not only improves the training
	speed of word2vec, but also makes the word vector more effective for the target
	task. Furthermore, the word2vec adopts a full connection between the input
	layer and the projection layer of the Continuous Bag-of-Words (CBOW) for
	acquiring the semantic information of the original sentence.},
  url       = {http://www.aclweb.org/anthology/S17-2114}
}