@InProceedings{cardellino-EtAl:2017:EACLshort,
  author    = {Cardellino, Cristian  and  Teruel, Milagro  and  Alonso Alemany, Laura  and  Villata, Serena},
  title     = {Legal NERC with ontologies, Wikipedia and curriculum learning},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {254--259},
  abstract  = {In this paper, we present a Wikipedia-based approach to develop resources for
	the legal domain. We establish a mapping between a legal domain ontology, LKIF
	(Hoekstra et al. 2007), and a Wikipedia-based ontology, YAGO (Suchanek et al.
	2007), and through that we populate LKIF. Moreover, we use the mentions of
	those entities in Wikipedia text to train a specific Named Entity Recognizer
	and Classifier. We find that this classifier works well in the Wikipedia, but,
	as could be expected, performance decreases in a corpus of judgments of the
	European Court of Human Rights. However, this tool will be used as a preprocess
	for human annotation.
	We resort to a technique called "curriculum learning" aimed to overcome
	problems of overfitting by learning increasingly more complex concepts.
	However, we find that in this particular setting, the method works best by
	learning from most specific to most general concepts, not the other way round.},
  url       = {http://www.aclweb.org/anthology/E17-2041}
}