@InProceedings{luan-ostendorf-hajishirzi:2017:EMNLP2017,
  author    = {Luan, Yi  and  Ostendorf, Mari  and  Hajishirzi, Hannaneh},
  title     = {Scientific Information Extraction with Semi-supervised Neural Tagging},
  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     = {2641--2651},
  abstract  = {This paper addresses the problem of extracting                                       
	     
	keyphrases
	from
	scientific
	articles and categorizing them as corresponding to a task, process, or
	material. We cast the problem as sequence tagging and introduce 
	semi-supervised methods to a neural tagging model, which builds on recent
	advances in named entity recognition. Since annotated training data is scarce
	in this domain, we introduce a graph-based semi-supervised algorithm together 
	with a data selection scheme to leverage unannotated articles. Both inductive
	and transductive semi-supervised learning strategies outperform
	state-of-the-art information extraction performance on the 2017 SemEval Task 10
	ScienceIE task.},
  url       = {https://www.aclweb.org/anthology/D17-1279}
}