@InProceedings{shu-xu-liu:2017:Short,
  author    = {Shu, Lei  and  Xu, Hu  and  Liu, Bing},
  title     = {Lifelong Learning CRF for Supervised Aspect Extraction},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {148--154},
  abstract  = {This paper makes a focused contribution to supervised aspect extraction. It
	shows that if the system has performed aspect extraction from many past domains
	and retained their results as knowledge, Conditional Random Fields (CRF) can
	leverage this knowledge in a lifelong learning manner to extract in a new
	domain markedly better than the traditional CRF without using this prior
	knowledge. The key innovation is that even after CRF training, the model can
	still improve its extraction with experiences in its applications.},
  url       = {http://aclweb.org/anthology/P17-2023}
}