@InProceedings{bellon-rodriguezesteban:2017:BioNLP,
  author    = {Bellon, Victor  and  Rodriguez-Esteban, Raul},
  title     = {One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text},
  booktitle = {Proceedings of the Biomedical NLP Workshop associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {49--54},
  abstract  = {We explored a new approach to named entity recognition based on hundreds of
	machine learning models, each trained to distinguish a single entity, and
	showed its application to gene name identification (GNI). The rationale for our
	approach, which we named ``one model per entity'' (OMPE), was that increasing
	the number of models would make the learning task easier for each individual
	model. Our training strategy leveraged freely-available database annotations
	instead of manually-annotated corpora. While its performance in our
	proof-of-concept was disappointing, we believe that there is enough room for
	improvement that such approaches could reach competitive performance while
	eliminating the cost of creating costly training corpora.},
  url       = {https://doi.org/10.26615/978-954-452-044-1_007}
}