Computational Reproducibility of Named Entity Recognition methods in the biomedical domain

• Autores: Ana M. García Serrano , Sebastian Hennig, Andreas Nürnberger
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 66, 2021, págs. 141-152
• Idioma: inglés
• Títulos paralelos:
  • Reproducción computacional de métodos de reconocimiento de entidades nombradas en un dominio biomédico
• Enlaces
  • Texto completo
• Resumen
  • español

    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.

  • English

    Unsupervised Named Entity Recognition (NER) approaches do not depend on labelled data to function properly but rather on a source of knowledge, in which promising candidates can be looked up to find the corresponding concept. In the biomedical domain knowledge source like this already exists; namely the Unified Medical Language System (UMLS). In this paper, three different unsupervised NER models using UMLS, namely MetaMap, cTakes and MetaMapLite are evaluated and compared from the results published by Demner-Fushman, Rogers and Aronson (2017) and Reategui and Ratte (2018). The Unsupervised Biomedical Named Entity Recognition framework (UB-NER) is developed, with which the results of the experiments of the three models, five datasets and two NER tasks are presented.

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