@InProceedings{calleja-EtAl:2017:RANLP,
  author    = {Calleja, Pablo  and  Garc\'{i}a Castro, Ra\'{u}l  and  Aguado-de-Cea, Guadalupe  and  G\'{o}mez-P\'{e}rez, Asunci\'{o}n},
  title     = {Role-based model for Named Entity Recognition},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {149--156},
  abstract  = {Named Entity Recognition (NER) poses new challenges in real-world documents in
	which there are entities with different roles according to their purpose or
	meaning. Retrieving all the possible entities in scenarios in which only a
	subset of them based on their role is needed, produces noise on the overall
	precision. This work proposes a NER model that relies on role classification
	models that support recognizing entities with a specific role. The proposed
	model has been implemented in two use cases using Spanish drug Summary of
	Product Characteristics: identification of therapeutic indications and
	identification of adverse reactions. The results show how precision is
	increased using a NER model that is oriented towards a specific role and
	discards entities out of scope.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_021}
}