LombardoGraphia: Automatic Classification of Lombard Orthography Variants

2026-03-30

AI Summary

This study addresses the lack of a standardized orthography for Lombard, a major barrier to developing natural language processing (NLP) resources for the language. To bridge this gap, the authors present the first automatic orthographic classification system for Lombard and introduce LombardoGraphia, the first large-scale annotated corpus comprising 11,186 samples spanning nine primary spelling variants. The approach integrates traditional machine learning and neural network models, leveraging multi-granularity text encodings and engineered features, with classifiers trained on cleaned Wikipedia data. The best-performing model achieves an overall accuracy of 96.06% and an average per-class accuracy of 85.78%, establishing a foundational framework for variant-aware NLP research in low-resource language varieties.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Lombard
• orthography variants
• underresourced language
• NLP resource development
• orthographic standardization

Innovation

• orthography classification
• underresourced languages
• Lombard
• NLP for minority languages
• curated corpus