@inproceedings{dolinska-bernhard-2024-pos,
    title = "{POS} Tagging for the Endangered Dagur Language",
    author = "Doli{\'n}ska, Joanna  and
      Bernhard, Delphine",
    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.1130",
    pages = "12906--12916",
    abstract = "The application of natural language processing tools opens new ways for the documentation and revitalization of under-resourced languages. In this article we aim to investigate the feasibility of automatic part-of-speech (POS) tagging for Dagur, which is an endangered Mongolic language spoken mainly in northeast China, with no official written standard for all Dagur dialects. We present a new manually annotated corpus for Dagur, which includes about 1,200 tokens, and detail the decisions made during the annotation process. This corpus is used to test transfer of models from other languages, especially from Buryat, which is currently the only Mongolic language included in the Universal Dependencies corpora. We applied the models trained by de Vries et al. (2022) to the Dagur corpus and continued training these models on Buryat. We analyse the results with respect to language families, script and POS distribution, in three different zero-shot settings: (1) unrelated, (2) related and (3) unrelated+related language.",
}

The application of natural language processing tools opens new ways for the documentation and revitalization of under-resourced languages. In this article we aim to investigate the feasibility of automatic part-of-speech (POS) tagging for Dagur, which is an endangered Mongolic language spoken mainly in northeast China, with no official written standard for all Dagur dialects. We present a new manually annotated corpus for Dagur, which includes about 1,200 tokens, and detail the decisions made during the annotation process. This corpus is used to test transfer of models from other languages, especially from Buryat, which is currently the only Mongolic language included in the Universal Dependencies corpora. We applied the models trained by de Vries et al. (2022) to the Dagur corpus and continued training these models on Buryat. We analyse the results with respect to language families, script and POS distribution, in three different zero-shot settings: (1) unrelated, (2) related and (3) unrelated+related language.
Doli{\'n}ska, Joanna Bernhard, Delphine
POS Tagging for the Endangered Dagur Language