@InProceedings{gan-gong:2017:I17-1,
  author    = {Gan, Ling  and  Gong, Houyu},
  title     = {Text Sentiment Analysis based on Fusion of Structural Information and Serialization Information},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {336--341},
  abstract  = {Tree-structured Long Short-Term Memory (Tree-LSTM) has been proved to be an
	effective method in the sentiment analysis task. It extracts structural
	information on text, and uses Long Short-Term Memory (LSTM) cell to prevent
	gradient vanish. However, though combining the LSTM cell, it is still a kind of
	model that extracts the structural information and almost not extracts
	serialization information. In this paper, we propose three new models in order
	to combine those two kinds of information: the structural information generated
	by the Constituency Tree-LSTM and the serialization information generated by
	Long-Short Term Memory neural network. Our experiments show that combining
	those two kinds of information can give contributes to the performance of the
	sentiment analysis task compared with the single Constituency Tree-LSTM model
	and the LSTM model.},
  url       = {http://www.aclweb.org/anthology/I17-1034}
}