Métodos de aprendizaje profundo para la extracción de nombres metafóricos de flores y plantas

  1. Ranasinghe, Tharindu
  2. Mitkov, Ruslan
  3. Haddad, Amal Haddad
  4. Premasiri, Damith
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2023

Número: 71

Páginas: 261-271

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openRUA editor

Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

El dominio de la Botánica es rico en términos metafóricos. Estos términos tienen un papel importante en la descripción e identificación de flores y plantas. Sin embargo, la identificación de este tipo de términos en el discurso es una tarea difícil. Esto puede conducir a errores en los procesos de traducción y otras tareas lexicográficas. Este proceso es aún más difícil cuando se trata de traducción automática, tanto en el caso de las unidades monoléxicas, como en el caso de las unidades multiléxicas. Uno de los desafíos a los que se enfrentan las aplicaciones del Procesamiento del Lenguaje Natural y las tecnologías de Traducción Automática es la identificación de términos basados en metáfora a través de métodos de aprendizaje profundo. En este estudio, tenemos el objetivo de rellenar este vacío a través del uso de trece modelos populares basados en transformadores, además del ChatGPT. Asimismo, demostramos que los modelos discriminativos aportan mejores resultados que los modelos de GPT-3.5. El mejor resultado alcanzó una puntuación de 92,2349% F1 en las tareas de identificación de nombres metafóricos de flores y plantas.

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Fuente de los datos: Dialnet