@InProceedings{gao-zhang-xiao:2017:I17-1,
  author    = {Gao, Yuze  and  Zhang, Yue  and  Xiao, Tong},
  title     = {Implicit Syntactic Features for Target-dependent Sentiment Analysis},
  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     = {516--524},
  abstract  = {Targeted sentiment analysis investigates the sentiment polarities on given
	target mentions from input texts. Different from sentence level sentiment, it
	offers more fine-grained knowledge on each entity mention. While early work
	leveraged syntactic information, recent research has used neural representation
	learning to induce features automatically, thereby avoiding error propagation
	of syntactic parsers, which are particularly severe on social media texts.
	We study a method to leverage syntactic information without explicitly building
	the parser outputs, by training an encoder-decoder structure parser model on
	standard syntactic treebanks, and then leveraging its hidden encoder layers
	when analysing tweets. Such hidden vectors do not contain explicit syntactic
	outputs, yet encode rich syntactic features. We use them to augment the inputs
	to a baseline state-of-the-art targeted sentiment classifier, observing
	significant improvements on various benchmark datasets. We obtain the best
	accuracies on all test sets.},
  url       = {http://www.aclweb.org/anthology/I17-1052}
}