@InProceedings{nguyen-nguyen:2017:SemEval,
  author    = {Nguyen, Khoa  and  Nguyen, Dang},
  title     = {UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR},
  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     = {909--913},
  abstract  = {This paper describes the improvements that we have applied on CAMR baseline
	parser (Wang et al., 2016) at Task 8 of SemEval-2016. Our objective is to
	increase the performance of CAMR when parsing sentences from scientific
	articles, especially articles of biology domain more accurately. To achieve
	this goal, we built two wrapper layers for CAMR. The first layer, which covers
	the input data, will normalize, add necessary information to the input
	sentences to make the input dependency parser and the aligner better handle
	reference citations, scientific figures, formulas, etc. The second layer, which
	covers the output data, will modify and standardize output data based on a list
	of scientific concept fixing patterns. This will help CAMR better handle
	biological concepts which are not in the training dataset. Finally, after
	applying our approach, CAMR has scored 0.65 F-score on the test set of
	Biomedical training data and 0.61 F-score on the official blind test dataset.},
  url       = {http://www.aclweb.org/anthology/S17-2156}
}