LombardoGraphia: Automatic Classification of Lombard Orthography Variants

TL;DR

This paper introduces LombardoGraphia, a curated Lombard language corpus and models for automatic orthography classification, addressing the lack of standardization in Lombard orthography for NLP development.

Contribution

It presents the first study on Lombard orthography classification, including a new dataset, models, and analysis of their performance.

Findings

Best models achieve 96.06% overall accuracy.

High accuracy on majority classes but challenges remain for minority classes.

Curated corpus enables future Lombard NLP resource development.

Abstract

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.