@inproceedings{garcia-holgado-vergez-couret-2024-empowering,
    title = "Empowering Low-Resource Regional Languages with Lexicons : A Comparative Study of {NLP} Tools for Morphosyntactic Analysis",
    author = "Garcia Holgado, Cristina  and
      Vergez-Couret, Marianne",
    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://preview.aclanthology.org/fix-sig-urls/2024.lrec-main.510/",
    pages = "5747--5756",
    abstract = "We investigate the effect of integrating lexicon information to an extremely low-resource language when annotated data is scarce for morpho-syntactic analysis. Obtaining such data and linguistic resources for these languages are usually constrained by a lack of human and financial resources making this task particularly challenging. In this paper, we describe the collection and leverage of a bilingual lexicon for Poitevin-Saintongeais, a regional language of France, to create augmented data through a neighbor-based distributional method. We assess this lexicon-driven approach in improving POS tagging while using different lexicon and augmented data sizes. To evaluate this strategy, we compare two distinct paradigms: neural networks, which typically require extensive data, and a conventional probabilistic approach, in which a lexicon is instrumental in its performance. Our findings reveal that the lexicon is a valuable asset for all models, but in particular for neural, demonstrating an enhanced generalization across diverse classes without requiring an extensive lexicon size."
}