@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://doi.org/10.26615/978-954-452-044-1_007",
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.",
}
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%0 Conference Proceedings
%T One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text
%A Bellon, Victor
%A Rodriguez-Esteban, Raul
%Y Boytcheva, Svetla
%Y Cohen, Kevin Bretonnel
%Y Savova, Guergana
%Y Angelova, Galia
%S Proceedings of the Biomedical NLP Workshop associated with RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F bellon-rodriguez-esteban-2017-one
%X 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.
%R 10.26615/978-954-452-044-1_007
%U https://doi.org/10.26615/978-954-452-044-1_007
%P 49-54
Markdown (Informal)
[One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text](https://doi.org/10.26615/978-954-452-044-1_007) (Bellon & Rodriguez-Esteban, RANLP 2017)
ACL