Overview of ADoBo 2021:: Automatic Detection of Unassimilated Borrowings in the Spanish Press

• Autores: Constantine Lignos, Jordi Porta Ribalta, Elena Álvarez Mellado, Luis Espinosa Anke, Julio Gonzalo Arroyo
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 67, 2021, págs. 277-285
• Idioma: inglés
• Títulos paralelos:
  • Resumen de ADoBo 2021:: detección automática de préstamos léxicos no asimilados en la prensa española
• Enlaces
  • Texto completo
• Resumen
  • español

    En este artículo presentamos los resultados de ADoBo 2021, la tarea compartida de IberLEF 2021 sobre detección de préstamos léxicos en la prensa española. En esta tarea abordamos la detección de préstamos como un problema de etiquetado de secuencias. A los participantes de la tarea se les proporcionó un corpus de prensa española anotado con préstamos léxicos no asimilados (mayoritariamente anglicismos) siguiendo el esquema BIO. Recibimos nueve sistemas distintos provenientes de cuatro equipos diferentes. Los resultados obtenidos oscilan entre los 37 y los 85 puntos de valor F1, lo que indica que la detección de préstamos léxicos es un problema no resuelto (sobre todo cuando se abordan préstamos no vistos anteriormente) y que el trabajo lexicográfico tradicional podría beneficiarse de incorporar las técnicas actuales del PLN.

  • English

    This paper summarizes the main findings of the ADoBo 2021 shared task, proposed in the context of IberLef 2021. In this task, we invited participants to detect lexical borrowings (coming mostly from English) in Spanish newswire texts. This task was framed as a sequence classification problem using BIO encoding. We provided participants with an annotated corpus of lexical borrowings which we split into training, development and test splits. We received submissions from 4 teams with 9 different system runs overall. The results, which range from F1 scores of 37 to 85, suggest that this is a challenging task, especially when out-of-domain or OOV words are considered, and that traditional methods informed with lexicographic information would benefit from taking advantage of current NLP trends.

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