@InProceedings{hsu-EtAl:2017:EACLshort,
  author    = {Hsu, Shiou Tian  and  Moon, Changsung  and  Jones, Paul  and  Samatova, Nagiza},
  title     = {A Hybrid CNN-RNN Alignment Model for Phrase-Aware Sentence Classification},
  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     = {443--449},
  abstract  = {The success of sentence classification often depends on understanding both the
	syntactic and semantic properties of word-phrases. Recent progress on this task
	has been based on exploiting the grammatical structure of sentences but often
	this structure is difficult to parse and noisy. In this paper, we propose a
	structure-independent `Gated Representation Alignment' (GRA) model that blends
	a phrase-focused Convolutional Neural Network (CNN) approach with
	sequence-oriented Recurrent Neural Network (RNN). Our novel alignment mechanism
	allows the RNN to selectively include phrase information in a word-by-word
	sentence representation, and to do this without awareness of the syntactic
	structure. An empirical evaluation of GRA shows higher prediction accuracy (up
	to $4.6\%$) of fine-grained sentiment ratings, when compared to other
	structure-independent baselines. We also show comparable results to several
	structure-dependent methods. Finally, we analyzed the effect of our alignment
	mechanism and found that this is critical to the effectiveness of the CNN-RNN
	hybrid.},
  url       = {http://www.aclweb.org/anthology/E17-2071}
}