@InProceedings{shu-xu-liu:2017:EMNLP2017,
  author    = {Shu, Lei  and  Xu, Hu  and  Liu, Bing},
  title     = {DOC: Deep Open Classification of Text Documents},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2911--2916},
  abstract  = {Traditional supervised learning makes the closed-world assumption that the
	classes appeared in the test data must have appeared in training. This also
	applies to text learning or text classification. As learning is used
	increasingly in dynamic open environments where some new/test documents may not
	belong to any of the training classes, identifying these novel documents during
	classification presents an important problem. This problem is called open-world
	classification or open classification. This paper proposes a novel deep
	learning based approach. It outperforms existing state-of-the-art techniques
	dramatically.},
  url       = {https://www.aclweb.org/anthology/D17-1314}
}