@InProceedings{gunes-furche-orsi:2016:COLING,
  author    = {Gunes, Omer  and  Furche, Tim  and  Orsi, Giorgio},
  title     = {Structured Aspect Extraction},
  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     = {2321--2332},
  abstract  = {Aspect extraction identifies relevant features from a textual description of an
	entity, e.g., a phone, and is typically targeted to product descriptions,
	reviews, and other short texts as an enabling task for, e.g., opinion mining
	and information retrieval.
	Current aspect extraction methods mostly focus on aspect terms and often
	neglect interesting modifiers of the term or embed them in the aspect term
	without proper distinction. Moreover, flat syntactic structures are often
	assumed, resulting in inaccurate extractions of complex aspects.
	This paper studies the problem of structured aspect extraction, a variant of
	traditional aspect extraction aiming at a fine-grained extraction of complex
	(i.e., hierarchical) aspects.
	We propose an unsupervised and scalable method for structured aspect extraction
	consisting of statistical noun phrase clustering, cPMI-based noun phrase
	segmentation, and hierarchical pattern induction.
	Our evaluation shows a substantial improvement over existing methods in terms
	of both quality and computational efficiency.},
  url       = {http://aclweb.org/anthology/C16-1219}
}