@InProceedings{giannakopoulos-EtAl:2017:WASSA2017,
  author    = {Giannakopoulos, Athanasios  and  Musat, Claudiu  and  Hossmann, Andreea  and  Baeriswyl, Michael},
  title     = {Unsupervised Aspect Term Extraction with B-LSTM \& CRF using Automatically Labelled Datasets},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {180--188},
  abstract  = {Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and
	is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA)
	contest. The small amount of available datasets for supervised ATE and the
	costly human annotation for aspect term labelling give rise to the need for
	unsupervised ATE. In this paper, we introduce an architecture that achieves
	top-ranking performance for supervised ATE. Moreover, it can be used
	efficiently as feature extractor and classifier for unsupervised ATE. Our
	second contribution is a method to automatically construct datasets for ATE. We
	train a classifier on our automatically labelled datasets and evaluate it on
	the human annotated SemEval ABSA test sets. Compared to a strong rule-based
	baseline, we obtain a dramatically higher F-score and attain precision values
	above 80%. Our unsupervised method beats the supervised ABSA baseline from
	SemEval, while preserving high precision scores.},
  url       = {http://www.aclweb.org/anthology/W17-5224}
}