@InProceedings{berend:2017:SemEval,
  author    = {Berend, G\'{a}bor},
  title     = {SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation},
  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     = {990--994},
  abstract  = {In this paper we introduce our system participating at the 2017 SemEval shared
	task on keyphrase extraction from scientific documents.
	We aimed at the creation of a keyphrase extraction approach which relies on as
	little external resources as possible. Without applying any hand-crafted
	external resources, and only utilizing a transformed version of word embeddings
	trained at Wikipedia, our proposed system manages to perform among the best
	participating systems in terms of precision.},
  url       = {http://www.aclweb.org/anthology/S17-2173}
}