@inproceedings{kruger-etal-2025-improving,
    title = "Improving Online Job Advertisement Analysis via Compositional Entity Extraction",
    author = {Kr{\"u}ger, Kai  and
      Binnewitt, Johanna  and
      Ehmann, Kathrin  and
      Winnige, Stefan  and
      Akbik, Alan},
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1375/",
    pages = "27035--27053",
    ISBN = "979-8-89176-332-6",
    abstract = "We propose a compositional entity modeling framework for requirement extraction from online job advertisements (OJAs), representing complex, tree-like structures that connect atomic entities via typed relations. Based on this schema, we introduce GOJA, a manually annotated dataset of 500 German job ads that captures roles, tools, experience levels, attitudes, and their functional context. We report strong inter-annotator agreement and benchmark transformer models, demonstrating the feasibility of learning this structure. A focused case study on AI-related requirements illustrates the analytical value of our approach for labor market research."
}