Overview of ADoBo at IberLEF 2025: Automatic Detection of Anglicisms in Spanish
This addresses the challenge of identifying anglicisms in Spanish for natural language processing applications, but it is incremental as it summarizes a shared task with existing methods.
The paper tackled the problem of automatically detecting English lexical borrowings (anglicisms) in Spanish journalistic texts, with results showing F1 scores ranging from 0.17 to 0.99 across different systems.
This paper summarizes the main findings of ADoBo 2025, the shared task on anglicism identification in Spanish proposed in the context of IberLEF 2025. Participants of ADoBo 2025 were asked to detect English lexical borrowings (or anglicisms) from a collection of Spanish journalistic texts. Five teams submitted their solutions for the test phase. Proposed systems included LLMs, deep learning models, Transformer-based models and rule-based systems. The results range from F1 scores of 0.17 to 0.99, which showcases the variability in performance different systems can have for this task.