@InProceedings{qian-EtAl:2017:Long,
  author    = {Qian, Qiao  and  Huang, Minlie  and  Lei, Jinhao  and  Zhu, Xiaoyan},
  title     = {Linguistically Regularized LSTM for Sentiment Classification},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1679--1689},
  abstract  = {This paper deals with sentence-level sentiment classification. Though a variety
	of neural network models have been proposed recently, however, previous models
	either depend on expensive phrase-level annotation, most of which has
	remarkably degraded performance when trained with only sentence-level
	annotation; or do not fully employ linguistic resources (e.g., sentiment
	lexicons, negation words, intensity words). In this paper, we propose simple
	models trained with sentence-level annotation, but also attempt to model the
	linguistic role of sentiment lexicons, negation words, and intensity words.
	Results show that our models are able to capture the linguistic role of
	sentiment words, negation words, and intensity words in sentiment expression.},
  url       = {http://aclweb.org/anthology/P17-1154}
}