@InProceedings{gu-EtAl:2018:C18-11,
  author    = {Gu, Shuqin  and  Zhang, Lipeng  and  Hou, Yuexian  and  Song, Yin},
  title     = {A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {774--784},
  abstract  = {Aspect-level sentiment analysis aims to distinguish the sentiment polarity of each specific aspect term in a given sentence. Both industry and academia have realized the importance of the relationship between aspect term and sentence, and made attempts to model the relationship by designing a series of attention models. However, most existing methods usually neglect the fact that the position information is also crucial for identifying the sentiment polarity of the aspect term. When an aspect term occurs in a sentence, its neighboring words should be given more attention than other words with long distance. Therefore, we propose a position-aware bidirectional attention network (PBAN) based on bidirectional GRU.},
  url       = {http://www.aclweb.org/anthology/C18-1066}
}