@InProceedings{ammar-EtAl:2017:SemEval,
  author    = {Ammar, Waleed  and  Peters, Matthew  and  Bhagavatula, Chandra  and  Power, Russell},
  title     = {The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {592--596},
  abstract  = {This paper describes our submission for
	the ScienceIE shared task (SemEval-
	2017 Task 10) on entity and relation
	extraction from scientific papers. Our
	model is based on the end-to-end relation
	extraction model of Miwa and Bansal
	(2016) with several enhancements such as
	semi-supervised learning via neural language
	models, character-level encoding,
	gazetteers extracted from existing knowledge
	bases, and model ensembles. Our of-
	ficial submission ranked first in end-to-end
	entity and relation extraction (scenario 1),
	and second in the relation-only extraction
	(scenario 3).},
  url       = {http://www.aclweb.org/anthology/S17-2097}
}