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One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text
2017
Proceedings of the Biomedical NLP Workshop
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 freelyavailable database annotations instead of manually-annotated corpora.
doi:10.26615/978-954-452-044-1_007
dblp:conf/ranlp/BellonR17
fatcat:uwhum2pm3jhztf5yp4apmbqzc4