@InProceedings{barik-marsi:2017:SemEval,
  author    = {Barik, Biswanath  and  Marsi, Erwin},
  title     = {NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents},
  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     = {965--968},
  abstract  = {This paper presents our relation extraction system for subtask C of
	SemEval-2017 Task 10: ScienceIE. Assuming that the keyphrases are already
	annotated in the input data, our work explores a  wide range of linguistic
	features, applies various feature selection techniques, optimizes the hyper
	parameters and class weights and experiments with different problem
	formulations (single classification model vs individual classifiers for each
	keyphrase type, single-step classifier vs pipeline classifier for hyponym
	relations). Performance of five popular classification algorithms are evaluated
	for each problem formulation along with feature selection. The best setting
	achieved an F1 score of 71.0% for synonym and 30.0% for hyponym relation on the
	test data.},
  url       = {http://www.aclweb.org/anthology/S17-2168}
}