LombardoGraphia: Automatic Classification of Lombard Orthography Variants

Edoardo Signoroni; Pavel Rychlý

cs.CLMar 30, 2026

LombardoGraphia: Automatic Classification of Lombard Orthography Variants

Authors: Edoardo Signoroni, Pavel Rychlý

Organizations: Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czechia

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.

Figures & tables

Orthography	Sample Text
MILCLASS	Sera settada in terra col coo in man, e i gombed sui genœucc: me ziffolava el vent in di cavij: demaneman che vegneva on quaj bôff, el me portava comè ona vôs che vegneva de lontan; […] "I was seating on the ground with the head in the hands, and the elbows on the knees: as it came in whiffs, it brought me as a voice from afar; […]"
LOCC	Sera setada in tera cul coo in man, e i gumbed süi genögg: me zifulava el vent in di cavii: demaneman che vegneva un quai buf, el me purtava cumè una vus che vegneva de luntan; […]
SL	S’era setada in terra col coo in man, e i gombeds sui jenœgg: me cifolava el vent ind i cavei: demaneman qe vegneva un quai bof, al me portava comè una vox qe vegneva de lontan; […]
NOL	S’era setada in terra col coo in man, e i gombet sui sgenoeugg: me scifolava el vent in di cavej: demaneman che vegneva un quaj bof, el me portava comè una vos che vegneva de lontan; […]
LSI	Cula paròla Ticines a sa inteend i dialett che i è parlaa in Tesin (vün dai 26 cantón svizzer) e in Mesulcina e Calanca (dó vall dal Canton Grison). […] "With the word Ticines , we mean the dialects spoken in Ticino (one of the 26 Swiss cantons) and in Mesolcina and Calanca (two valleys of Grisons)"
LORUNIF	L’ortografìa orientàl ünificàda l’è ’n tentatìf de dàga ’na rispòsta al bizògn de ’n sistéma de scritüra bù per töte le varietà piö parlàde del Lombàrt Orientàl […] "The unified oriental orthography is an attempt to answer the need for a writing system for all the most spoken varieties of Eastern Lombard."

Table 1: Summary of the Lombard orthographies in the dataset. The first column give their abbreviated name, while the second shows a text sample for each one. When available (MILCLASS, LOCC, SL, NOL) we used the same text. 19 19 19 From La Fuggitiva ("The Fugitive") by Tommaso Grossi (1791-1853). Retrieved from https://lmo.wikipedia.org/wiki/Dialett_milanes . Own translation.

Class	Type	Total Lines	Train		Valid		Test		Dataset Total		Removed
Class	Type	Total Lines	N	%	N	%	N	%	N	%	N	%
MILCLASS	Local	79,196	3,606	40.29	471	42.13	446	39.89	4,523	40.43	74,673	94.29
LOCC	Macro-dialectal	34,794	2,907	32.48	345	30.86	380	33.99	3,632	32.47	31,162	89.56
LORUNIF	Macro-dialectal	76,455	1,901	21.24	240	21.47	229	20.48	2,370	21.19	74,085	96.90
SL	Pan-Lombard	3,761	174	1.94	22	1.97	16	1.43	212	1.90	3,549	94.36
NOL	Pan-Lombard	2,349	109	1.22	9	0.81	8	0.72	126	1.13	2,223	94.64

Table 2: Distribution of orthographic classes across train, validation, and test sets. Total Lines shows the number of lines in the Wikipedia corpus before filtering. Dataset Total reports the number of lines in the cleaned corpus. Removed shows lines filtered out due to quality criteria (N) and the percentage removed relative to Total Lines (%). The no_tag category contains lines without orthography tags that were excluded from the dataset.

Model	MILCLASS	LOCC	LORUNIF	SL	NOL	CRES	BREMOD	BERGDUC	Overall	Avg Class
Traditional Models - Logistic Regression
Log. byte	97.31	92.89	89.08	100.0	75.00	94.74	53.85	83.33	93.38	85.78
Log. byte+char+word	97.31	94.74	94.76	100.0	75.00	100.0	46.15	66.67	95.08	84.33
Log. char	95.07	93.42	84.28	100.0	75.00	94.74	53.85	83.33	91.67	84.96
Log. word	94.62	94.21	88.65	93.75	37.50	94.74	69.23	66.67	92.39	79.92
Traditional Models - Support Vector Machine

Table 3: Complete per-class accuracy (%) across all 24 models, with overall and average class accuracy. Bold indicates the best performance per metric. Classes are ordered by sample size. Overall accuracy measures performance on all test samples, while high average class accuracy shows consistent performance across classes.

Figure 1: Boxplot of the results for all models. Traditional models are in purple, and neural models are in blue.

Low-Resource Language Processing

Low-resource language processing builds and evaluates language technologies for languages with limited data, tools, or digital representation.

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"Chi nas dal soch el sent de legn" -- Auditing Text Corpora for Lombard

Edoardo Signoroni, Pavel Rychlý

NLP Centre, Faculty of Informatics

Training Data Curation Low-Resource Language Processing Corpus Linguistics

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Several of the world's languages are still under-resourced in terms of Natural Language Processing (NLP) tools. This is mostly due to the lack of high-quality datasets to train, develop, and evaluate systems and models for several tasks, such as Machine Translation (MT). We conduct a manual audit of the parallel and monolingual corpora available for Lombard, an under-resourced language continuum from Italy. Our analysis reveals that the perceived abundance of web-scraped data is an illusion, with massive datasets plagued by severe language misidentification, boilerplate text, and non-linguistic noise. Furthermore, we analyze the orthographic composition of the valid Lombard portions across web-scraped datasets, curated corpora, and benchmarks. Our findings show conflicting orthographical systems and severe representational bias across all corpora: high-quality data is heavily skewed towards Western Lombard varieties, with Eastern ones left on the margins. This underscores the need for variety-aware, community-driven data curation rather than purely quantity-driven scraping.

Abstract

Low-Resource Language Processing

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