Resumen de TESTLINK en IberLEF 2023Creación de relaciones entre análisis de laboratorio y mediciones clínicas y sus resultados

  1. Zanoli, Roberto
  2. Karunakaran, Goutham
  3. Altuna Díaz, Begoña
  4. Agerri Gascón, Rodrigo
  5. Salas Espejo, Lidia
  6. Saiz, José Javier
  7. Lavelli, Alberto
  8. Magnini, Bernardo
  9. Speranza, Manuela
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2023

Número: 71

Páginas: 313-320

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

La tarea TESTLINK de IberLEF2023 se centra en la extracción de relaciones de casos clínicos en español y euskera. La tarea consiste en identificar resultados y medidas clínicas y relacionarlos con las pruebas y mediciones de las que se obtuvieron. Tres equipos han participado en la tarea y se han evaluado varios modelos (supervisados) de aprendizaje profundo. Curiosamente, ninguno de los equipos exploró el uso del aprendizaje few-shot. La evaluación muestra que el fine-tuning en el dominio y conjuntos de datos de entrenamiento más grandes mejoran los resultados. De hecho, el hecho de que los modelos supervisados superaran significativamente la baseline basada en el aprendizaje few-shot muestra el papel crucial que aún desempeña la disponibilidad de datos de entrenamiento anotados.

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Fuente de los datos: Dialnet