@inproceedings{zhou-etal-2026-textminex,
    title = "{T}ext{M}ine{X}: Data, Evaluation Framework and Ontology-guided {LLM} Pipeline for Humanitarian Mine Action",
    author = {Zhou, Chenyue  and
      Solmaz, G{\"u}rkan  and
      Cirillo, Flavio  and
      Gashteovski, Kiril  and
      F{\"u}rst, Jonathan},
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.130/",
    pages = "2505--2523",
    ISBN = "979-8-89176-386-9",
    abstract = "Humanitarian Mine Action (HMA) addresses the challenge of detecting and removing landmines from conflict regions. Much of the life-saving operational knowledge produced by HMA agencies is buried in unstructured reports, limiting the transferability of information between agencies. To address this issue, we propose TextMineX: the first dataset, evaluation framework and ontology-guided large language model (LLM) pipeline for knowledge extraction from text in the HMA domain. TextMineX structures HMA reports into (subject, relation, object)-triples, thus creating domain-specific knowledge. To ensure real-world relevance, we utilized the dataset from our collaborator Cambodian Mine Action Centre (CMAC). We further introduce a bias-aware evaluation framework that combines human-annotated triples with an LLM-as-Judge protocol to mitigate position bias in reference-free scoring. Our experiments show that ontology-aligned prompts improve extraction accuracy by up to 44.2{\%}, reduce hallucinations by 22.5{\%}, and enhance format adherence by 20.9{\%} compared to baseline models. We publicly release the dataset and code."
}