@InProceedings{moreno-romaferri-moredapozo:2017:RANLP,
  author    = {Moreno, Isabel  and  Rom\'{a}-Ferri, Maria Teresa  and  Moreda Pozo, Paloma},
  title     = {A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information},
  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     = {510--518},
  abstract  = {This paper presents a Named Entity Classification system, which employs machine
	learning. Our methodology employs local entity information and profiles as
	feature set. All features are generated in an unsupervised manner. It is tested
	on two different data sets: (i) DrugSemantics Spanish corpus (Overall F1 =
	74.92), whose results  are in-line with the state of the art without employing
	external domain-specific resources. And, (ii) English CONLL2003 dataset
	(Overall F1 = 81.40), although our results are lower than
	previous work, these are reached without external knowledge or complex
	linguistic analysis. Last, using the same configuration for the two corpora,
	the difference of overall F1 is only 6.48 points (DrugSemantics = 74.92
	versus CoNLL2003 = 81.40). Thus, this result supports our hypothesis that our
	approach is language and domain independent and does not require any external
	knowledge or complex linguistic analysis.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_067}
}