Resumen de la tarea MentalRiskES en IberLEF 2023: Detección precoz del riesgo de trastornos mentales en español

• Autores: Luis Alfonso Ureña López , Arturo Montejo Ráez , Alba María Mármol Romero, Adrián Moreno Muñoz, Flor Miriam Plaza del Arco , M. Dolores Molina González, María Teresa Martín Valdivia
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 71, 2023, págs. 329-350
• Idioma: varios idiomas
• Títulos paralelos:
  • Overview of MentalRiskES at IberLEF 2023: Early Detection of Mental Disorders Risk in Spanish
• Enlaces
  • Texto completo
• Resumen
  • Multiple

    Este artículo presenta la tarea MentalRiskES en IberLEF 2023, como parte de la 39ª edición de la Conferencia Internacional de la Sociedad Española para el Procesamiento del Lenguaje Natural. El objetivo de esta competición es promover la detección temprana de trastornos mentales en español. Proponemos tres tareas de detección precoz: Tarea 1 para trastornos alimentarios, Tarea 2 para la depresión y Tarea 3 para identificar un trastorno que no desvelamos a los participantes (ansiedad) para observar la transferencia de conocimiento entre los distintos trastornos. Solicitamos medir emisiones de carbono para un desarrollo de modelos sostenible. En esta primera edición, 37 equipos se registraron, 18 enviaron predicciones y 16 presentaron artículos. La mayoría experimentó con Transformers, incluyendo características, ampliando datos y técnicas de preprocesamiento

  • English

    This paper presents the MentalRiskEs shared task organized at IberLEF 2023, as part of the 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023). The aim of this task is to promote the early detection of mental risk disorders in Spanish. We outline three detection tasks: Task 1 on eating disorders, Task 2 on depression, and Task 3 on an undisclosed disorder during the competition (anxiety) to observe the transfer of knowledge among the different disorders proposed. Furthermore, we asked participants to submit measurements of carbon emissions for their systems, emphasizing the need for sustainable NLP practices. In this first edition, 37 teams registered, 18 submitted results, and 16 presented papers. Most teams experimented with Transformers, including features, data augmentation, and preprocessing techniques

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