@inproceedings{millour-etal-2024-agettivu,
    title = "Agettivu, Aggitivu o Aghjettivu? {POS} Tagging {C}orsican Dialects",
    author = "Millour, Alice  and
      Brasile, Lorenza  and
      Ghia, Alberto  and
      Kevers, Laurent",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.52",
    pages = "600--608",
    abstract = "In this paper we present a series of experiments towards POS tagging Corsican, a less-resourced language spoken in Corsica and linguistically related to Italian. The first contribution is Corsican-POS, the first gold standard POS-tagged corpus for Corsica, composed of 500 sentences manually annotated with the Universal POS tagset. Our second contribution is a set of experiments and evaluation of POS tagging models which starts with a baseline model for Italian and is aimed at finding the best training configuration, namely in terms of the size and combination strategy of the existing raw and annotated resources. These experiments result in (i) the first POS tagger for Corsican, reaching an accuracy of 93.38{\%}, (ii) a quantification of the gain provided by the use of each available resource. We find that the optimal configuration uses Italian word embeddings further specialized with Corsican embeddings and trained on the largest gold corpus for Corsican available so far.",
}

In this paper we present a series of experiments towards POS tagging Corsican, a less-resourced language spoken in Corsica and linguistically related to Italian. The first contribution is Corsican-POS, the first gold standard POS-tagged corpus for Corsica, composed of 500 sentences manually annotated with the Universal POS tagset. Our second contribution is a set of experiments and evaluation of POS tagging models which starts with a baseline model for Italian and is aimed at finding the best training configuration, namely in terms of the size and combination strategy of the existing raw and annotated resources. These experiments result in (i) the first POS tagger for Corsican, reaching an accuracy of 93.38{\%}, (ii) a quantification of the gain provided by the use of each available resource. We find that the optimal configuration uses Italian word embeddings further specialized with Corsican embeddings and trained on the largest gold corpus for Corsican available so far.
Millour, Alice Brasile, Lorenza Ghia, Alberto Kevers, Laurent
Agettivu, Aggitivu o Aghjettivu? POS Tagging Corsican Dialects