Computational Reproducibility of Named Entity Recognition methods in the biomedical domain

  1. Ana García Serrano
  2. Sebastian Hennig
  3. Andreas Nürnberger
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2021

Issue: 66

Pages: 141-152

Type: Article

DIALNET GOOGLE SCHOLAR HANDLE: https://hdl.handle.net/10045/114248

More publications in: Procesamiento del lenguaje natural

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Abstract

Los enfoques para reconocimiento de entidades nombradas no supervisados (NER, por sus siglas en inglés) no dependen de corpus con datos etiquetados, sino de una fuente de conocimiento donde buscar candidatos prometedores para encontrar el concepto correspondiente. En el ámbito biomédico existe la fuente denominada “Sistema Unificado de Lenguaje Médico” (UMLS, por sus siglas en inglés). En este artículo, se evalúan y comparan tres modelos diferentes de NER no supervisados que utilizan UMLS, a saber, MetaMap, cTakes y MetaMapLite, a partir de los resultados publicados por Demner-Fushman, Rogers y Aronson (2017) y Reategui y Ratte (2018). Para ello se desarrolla el entorno Unsupervised Biomedical Named Entity Recognition (UB-NER), con el que se presentan resultados de los experimentos en los modelos, cinco datasets y dos tareas NER.

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Data source: Dialnet