@InProceedings{hazarika-EtAl:2018:N18-2,
  author    = {Hazarika, Devamanyu  and  Poria, Soujanya  and  Vij, Prateek  and  Krishnamurthy, Gangeshwar  and  Cambria, Erik  and  Zimmermann, Roger},
  title     = {Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {266--270},
  abstract  = {Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.},
  url       = {http://www.aclweb.org/anthology/N18-2043}
}