@InProceedings{wang-EtAl:2017:EMNLP20177,
  author    = {Wang, Chengyu  and  Fan, Yan  and  He, Xiaofeng  and  Zhou, Aoying},
  title     = {Learning Fine-grained Relations from Chinese User Generated Categories},
  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     = {2577--2587},
  abstract  = {User generated categories (UGCs) are short texts that reflect how people
	describe and organize entities, expressing rich semantic relations implicitly.
	While most methods on UGC relation extraction are based on pattern matching in
	English circumstances, learning relations from Chinese UGCs poses different
	challenges due to the flexibility of expressions. In this paper, we present a
	weakly supervised learning framework to harvest relations from Chinese UGCs. We
	identify is-a relations via word embedding based projection and inference,
	extract non-taxonomic relations and their category patterns by graph mining. We
	conduct experiments on Chinese Wikipedia and achieve high accuracy,
	outperforming state-of-the-art methods.},
  url       = {https://www.aclweb.org/anthology/D17-1273}
}