@inproceedings{blstak-etal-2025-dictionary,
    title = "When the Dictionary Strikes Back: A Case Study on {S}lovak Migration Location Term Extraction and {NER} via Rule-Based vs. {LLM} Methods",
    author = "Bl{\v{s}}t{\'a}k, Miroslav  and
      Kop{\v{c}}an, Jaroslav  and
      Suppa, Marek  and
      Havran, Samuel  and
      Findor, Andrej  and
      Takac, Martin  and
      Simko, Marian",
    editor = "Piskorski, Jakub  and
      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Nakov, Preslav  and
      Yangarber, Roman  and
      Marcinczuk, Michal",
    booktitle = "Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bsnlp-1.11/",
    pages = "91--100",
    ISBN = "978-1-959429-57-9",
    abstract = "This study explores the task of automatically extracting migration-related locations (source and destination) from media articles, focusing on the challenges posed by Slovak, a low-resource and morphologically complex language. We present the first comparative analysis of rule-based dictionary approaches (NLP4SK) versus Large Language Models (LLMs, e.g. SlovakBERT, GPT-4o) for both geographical relevance classification (Slovakia-focused migration) and specific source/target location extraction. To facilitate this research and future work, we introduce the first manually annotated Slovak dataset tailored for migration-focused locality detection. Our results show that while a fine-tuned SlovakBERT model achieves high accuracy for classification, specialized rule-based methods still have the potential to outperform LLMs for specific extraction tasks, though improved LLM performance with few-shot examples suggests future competitiveness as research in this area continues to evolve."
}