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• The LombardoGraphia corpus is the first curated dataset for automatic Lombard orthography classification, containing 11,186 Wikipedia samples tagged across 9 orthographic variants.method 95%
• The best classification models achieve 96.06% overall accuracy and 85.78% average class accuracy on the Lombard orthography classification task.result 90%
• Performance on minority orthography classes remains challenging due to data imbalance in the corpus.limitation 85%
• Lombard lacks a unified orthographic standard, with multiple competing systems creating challenges for NLP resource development.technical 95%
• The study trained and evaluated 24 classification models featuring both traditional and neural approaches with different feature and encoding levels.method 90%
• The corpus includes detailed metadata about orthographic systems and their geographic distributions.method 85%
• The work provides crucial infrastructure for building variety-aware NLP resources for Lombard.technical 80%
• The classification task can be framed as a language identification problem for similar language varieties.technical 75%

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