@InProceedings{eshel-EtAl:2017:CoNLL,
  author    = {Eshel, Yotam  and  Cohen, Noam  and  Radinsky, Kira  and  Markovitch, Shaul  and  Yamada, Ikuya  and  Levy, Omer},
  title     = {Named Entity Disambiguation for Noisy Text},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {58--68},
  abstract  = {We address the task of Named Entity Disambiguation (NED) for noisy text. 
	We present WikilinksNED, a large-scale NED dataset of text fragments from the
	web, which is significantly noisier and more challenging than existing
	news-based datasets. To capture the limited and noisy local context surrounding
	each mention, we design a neural model and train it with a novel method for
	sampling informative negative examples. We also describe a new way of
	initializing word and entity embeddings that significantly improves
	performance. Our model significantly outperforms existing state-of-the-art
	methods on WikilinksNED while achieving comparable performance on a smaller
	newswire
	dataset.
	Author{3}{Affiliation}},
  url       = {http://aclweb.org/anthology/K17-1008}
}