@InProceedings{yin-song-zhang:2017:SemEval,
  author    = {Yin, Yichun  and  Song, Yangqiu  and  Zhang, Ming},
  title     = {NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings},
  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     = {621--625},
  abstract  = {Recently, neural twitter sentiment classification has become one of
	state-of-thearts, which relies less feature engineering work compared with
	traditional methods. In this paper, we propose a simple and effective ensemble
	method to further boost the performances of neural models. We collect several
	word embedding sets which are publicly released (often are learned on different
	corpus) or constructed by running Skip-gram on released large-scale corpus. We
	make an assumption that different word embeddings cover different words and
	encode different semantic knowledge, thus using them together can improve the
	generalizations and performances of neural models. In the SemEval 2017, our
	method ranks 1st in Accuracy, 5th in AverageR. Meanwhile, the additional
	comparisons demonstrate the superiority of our model over these ones based
	on only one word embedding set. We release our code for the method
	duplicability.},
  url       = {http://www.aclweb.org/anthology/S17-2102}
}