@InProceedings{liu-zhang:2017:EACLshort,
  author    = {Liu, Jiangming  and  Zhang, Yue},
  title     = {Attention Modeling for Targeted Sentiment},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {572--577},
  abstract  = {Neural network models have been used for target-dependent sentiment analysis.
	Previous work focus on learning a target specific representation for a given
	input sentence which is used for classification. However, they do not
	explicitly model the contribution of each word in a sentence with respect to
	targeted sentiment polarities. We investigate an attention model to this end.
	In particular, a vanilla LSTM model is used to induce an attention value of the
	whole sentence. The model is further extended to differentiate left and right
	contexts given a certain target following previous work. Results show that by
	using attention to model the contribution of each word with respect to the
	target, our model gives significantly improved results over two standard
	benchmarks. We report the best accuracy for this task.},
  url       = {http://www.aclweb.org/anthology/E17-2091}
}