@inproceedings{principe-etal-2025-enhancing,
    title = "Enhancing Information Extraction with Large Language Models: A Comparison with Human Annotation and Rule-Based Methods in a Real Estate Case Study",
    author = "Principe, Renzo Alva  and
      Viviani, Marco  and
      Chiarini, Nicola",
    editor = "Alam, Mehwish  and
      Tchechmedjiev, Andon  and
      Gracia, Jorge  and
      Gromann, Dagmar  and
      di Buono, Maria Pia  and
      Monti, Johanna  and
      Ionov, Maxim",
    booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge",
    month = sep,
    year = "2025",
    address = "Naples, Italy",
    publisher = "Unior Press",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.ldk-1.25/",
    pages = "243--254",
    ISBN = "978-88-6719-333-2",
    abstract = "Information Extraction (IE) is a key task in Natural Language Processing (NLP) that transforms unstructured text into structured data. This study compares human annotation, rule-based systems, and Large Language Models (LLMs) for domain-specific IE, focusing on real estate auction documents. We assess each method in terms of accuracy, scalability, and cost-efficiency, highlighting the associated trade-offs. Our findings provide valuable insights into the effectiveness of using LLMs for the considered task and, more broadly, offer guidance on how organizations can balance automation, maintainability, and performance when selecting the most suitable IE solution."
}