Language changes faster than dictionaries can be revised, yet automatic tools still struggle to spot the subtle, short-term shifts in meaning that precede a formal update. We present a language-independent pipeline that detects word-sense shifts in large, time-stamped web corpora. The method couples a robust re-implementation of the Adaptive Skip-Gram model, which induces multiple sense vectors per lemma without any external inventory, with a second stage that tracks each sense through time under three alternative frequency normalizations. Linear Regression and the robust Mann-Kendall/Theil-Sen estimator then test whether a sense’s frequency slope deviates significantly from zero, producing a ranked list of headwords whose semantics are drifting.We evaluate the system on the English (12 B tokens) and Czech (1 B tokens) Timestamped corpora for May 2023-May 2025. Expert annotation of the top-100 candidates for each model variant shows that 50.7% of Czech and 25.7% of English headwords exhibit genuine sense shifts, despite web-scale noise.

We evaluate the capabilities of several small large language models (LLMs) to translate between Italian and six low-resource language varieties from Italy (Friulan, Ligurian, Lombard, Sicilian, Sardinian, and Venetian). Using recent benchmark datasets, such as FLORES+ and OLDI-Seed, we compare prompting and fine-tuning approaches for downstream translation, evaluated with CHRF scores. Our findings confirm that these LLMs struggle to translate into and from these low-resource language varieties. Pretraining and fine-tuning a small LLM did not yield improvements over a zero-shot baseline. These results underscore the need for further NLP research on Italy’s low-resource language varieties. As the digital divide continues to threaten the conservation of this diverse linguistic landscape, greater engagement with speaker communities to create better and more representative datasets is essential to boost the translation performance of current LLMs.

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.