LombardoGraphia: Automatic Classification of Lombard Orthography Variants
This work addresses challenges in NLP resource development for Lombard speakers by providing infrastructure for variety-aware tools, though it is incremental as it applies existing methods to a new dataset.
The paper tackled the lack of a unified orthographic standard for Lombard, an underresourced language variety, by creating the first automatic classification system for its orthography variants, achieving up to 96.06% overall accuracy.
Lombard, an underresourced language variety spoken by approximately 3.8 million people in Northern Italy and Southern Switzerland, lacks a unified orthographic standard. Multiple orthographic systems exist, creating challenges for NLP resource development and model training. This paper presents the first study of automatic Lombard orthography classification and LombardoGraphia, a curated corpus of 11,186 Lombard Wikipedia samples tagged across 9 orthographic variants, and models for automatic orthography classification. We curate the dataset, processing and filtering raw Wikipedia content to ensure text suitable for orthographic analysis. We train 24 traditional and neural classification models with various features and encoding levels. Our best models achieve 96.06% and 85.78% overall and average class accuracy, though performance on minority classes remains challenging due to data imbalance. Our work provides crucial infrastructure for building variety-aware NLP resources for Lombard.