@inproceedings{bellon-rodriguez-esteban-2017-one,
    title = "One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text",
    author = "Bellon, Victor  and
      Rodriguez-Esteban, Raul",
    editor = "Boytcheva, Svetla  and
      Cohen, Kevin Bretonnel  and
      Savova, Guergana  and
      Angelova, Galia",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-8007/",
    doi = "10.26615/978-954-452-044-1_007",
    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."
}