@InProceedings{yang-EtAl:2017:EACLshort2,
  author    = {Yang, Yinfei  and  Chen, Cen  and  Qiu, Minghui  and  Bao, Forrest},
  title     = {Aspect Extraction from Product Reviews Using Category Hierarchy Information},
  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     = {675--680},
  abstract  = {Aspect extraction abstracts the common properties of objects from corpora
	discussing them, such as reviews of products.
	Recent work on aspect extraction is leveraging the hierarchical relationship
	between products and their categories. 
	However, such effort focuses on the aspects of child categories but ignores
	those from parent categories.
	Hence, we propose an LDA-based generative topic model inducing the two-layer
	categorical information (CAT-LDA), to balance the aspects of both a parent
	category and its child categories.
	Our hypothesis is that child categories inherit aspects from parent categories,
	controlled by the hierarchy between them. 
	Experimental results on 5 categories of Amazon.com products show that both
	common aspects of parent category and the individual aspects of sub-categories
	can be extracted to align well with the common sense. 
	We further evaluate the manually extracted aspects of 16 products, resulting in
	an average hit rate of 79.10\%.},
  url       = {http://www.aclweb.org/anthology/E17-2107}
}