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Desvelando el reconocimiento de entidades con nombres en árabe utilizando el procesamiento del lenguaje natural con

Wala Bin Subait1, Nazir Ahmad2, Muhammad Swaileh A Alzaidi3

  • 1Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

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Resumen

Este estudio introduce una nueva técnica de IA para el reconocimiento de entidades con nombre árabe (NER) en el dialecto marroquí, mejorando significativamente la precisión. El método de optimización de Northern Goshawk con inteligencia artificial (NGOAI-ANER) mejora la precisión en la extracción de entidades de texto árabe.

Palabras clave:
Texto en árabeInteligencia artificialAprendizaje profundoReconocimiento de entidades con nombreOptimización del halcón del norte

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Área de la Ciencia:

  • Procesamiento del lenguaje natural (PNL)
  • Inteligencia artificial (IA)
  • Aprendizaje profundo

Sus antecedentes:

  • El reconocimiento de entidades con nombre (NER) es crucial para aplicaciones de PNL como la recuperación de datos.
  • El NER árabe presenta desafíos únicos debido a la complejidad lingüística, especialmente en los dialectos.
  • Los modelos de aprendizaje profundo existentes a menudo se centran en el árabe estándar moderno (MSA), descuidando los dialectos.

Objetivo del estudio:

  • Para introducir una técnica novedosa, la optimización de Northern Goshawk con inteligencia artificial para el reconocimiento de entidades con nombre árabe (NGOAI-ANER), para el dialecto marroquí.
  • Mejorar la precisión y la eficiencia de los sistemas NER árabes.
  • Para abordar las complejidades del dialecto árabe NER utilizando IA avanzada.

Principales métodos:

  • Técnicas de incrustación de palabras para representar el texto semánticamente.
  • Memoria a corto plazo de atención larga (SALSTM) para una identificación precisa de la entidad.
  • Modelo de optimización de Northern Goshawk (NGO) para el ajuste de hiperparámetros de los modelos DL.

Principales resultados:

  • La técnica NGOAI-ANER demostró un rendimiento superior en el conjunto de datos de DarNERcorp.
  • Logró una precisión del 97,86%, superando a los enfoques NER árabes existentes.
  • Validación de la eficacia y escalabilidad del modelo NER impulsado por IA propuesto.

Conclusiones:

  • La técnica NGOAI-ANER ofrece un avance significativo en el NER árabe dialectal.
  • La integración de algoritmos de optimización con el aprendizaje profundo aborda eficazmente los desafíos de NER.
  • Este enfoque proporciona una solución escalable y precisa para el reconocimiento de entidades con nombre árabe.