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Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing.
Grabar, Natalia; Grouin, Cyril.
  • Grabar N; STL, CNRS, Université de Lille, Domaine du Pont-de-bois, Villeneuve-d'Ascq cedex, France.
  • Grouin C; Université Paris Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France.
Yearb Med Inform ; 31(1): 254-260, 2022 Aug.
Artículo en Inglés | MEDLINE | ID: covidwho-2151185
ABSTRACT

OBJECTIVES:

Analyze the content of publications within the medical natural language processing (NLP) domain in 2021.

METHODS:

Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

RESULTS:

Four best papers have been selected in 2021. We also propose an analysis of the content of the NLP publications in 2021, all topics included.

CONCLUSIONS:

The main issues addressed in 2021 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as information extraction and use of information from social networks.
Asunto(s)

Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Procesamiento de Lenguaje Natural / COVID-19 Límite: Humanos Idioma: Inglés Revista: Yearb Med Inform Año: 2022 Tipo del documento: Artículo País de afiliación: S-0042-1742547

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Procesamiento de Lenguaje Natural / COVID-19 Límite: Humanos Idioma: Inglés Revista: Yearb Med Inform Año: 2022 Tipo del documento: Artículo País de afiliación: S-0042-1742547