@inproceedings{politov-etal-2025-revisiting,
    title = "Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages",
    author = {Politov, Andrei  and
      Shkalikov, Oleh  and
      J{\"a}kel, Rene  and
      F{\"a}rber, Michael},
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.nodalida-1.54/",
    pages = "499--507",
    ISBN = "978-9908-53-109-0",
    abstract = "Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for cross-lingual NER and can outperform multi-lingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we present a novel formalized projection approach of matching source entities with extracted target candidates. Through extensive experiments on two datasets spanning 57 languages, we demonstrated that our approach surpasses existing projection-based methods in low-resource settings. These findings highlight the robustness of projection-based data transfer as an alternative to model-based methods for cross-lingual named entity recognition in low-resource languages."
}