@InProceedings{xiong-EtAl:2016:COLING,
  author    = {Xiong, Shufeng  and  Zhang, Yue  and  JI, Donghong  and  Lou, Yinxia},
  title     = {Distance Metric Learning for Aspect Phrase Grouping},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2492--2502},
  abstract  = {Aspect phrase grouping is an important task in aspect-level sentiment analysis.
	It is a challenging problem due to polysemy and context dependency. We propose
	an Attention-based Deep Distance Metric Learning (ADDML) method, by considering
	aspect phrase representation as well as context representation. First,
	leveraging the characteristics of the review text, we automatically generate
	aspect phrase sample pairs for distant supervision. Second, we feed word
	embeddings of aspect phrases and their contexts into an attention-based neural
	network to learn feature representation of contexts. Both aspect phrase
	embedding and context embedding are used to learn a deep feature subspace for
	measure the distances between aspect phrases for K-means clustering.
	Experiments on four review datasets show that the proposed method outperforms
	state-of-the-art strong baseline methods.},
  url       = {http://aclweb.org/anthology/C16-1235}
}