@InProceedings{segurabedmar-colonruiz-martinez:2017:SemEval,
  author    = {Segura-Bedmar, Isabel  and  Col\'{o}n-Ruiz, Crist\'{o}bal  and  Mart\'{i}nez, Paloma},
  title     = {LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network},
  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     = {947--950},
  abstract  = {This paper describes the system presented by the LABDA group at SemEval 2017
	Task 10 ScienceIE, specifically for the subtasks of identification and
	classification of keyphrases from scientific articles.
	  For the task of identification, we use the BANNER tool, a named entity
	recognition system, which
	  is based on conditional random fields (CRF) and has obtained successful
	results in the biomedical domain. To classify keyphrases, we study the UMLS
	semantic network and propose a possible linking between the keyphrase types and
	the UMLS semantic groups. Based on this semantic linking, we create a
	dictionary for each keyphrase type. Then, a feature indicating if a token is
	found in one of these dictionaries is incorporated to feature set used by the
	BANNER tool. The final results on the test dataset show that our system 
	  still needs to be improved, but the conditional random fields and,
	consequently, 
	  the BANNER system can be used as a first approximation to identify and
	classify 
	  keyphrases.},
  url       = {http://www.aclweb.org/anthology/S17-2164}
}