TextMine: LLM-Powered Knowledge Extraction for Humanitarian Mine Action

ACL ARR 2025 May Submission648 Authors

Abstract: Humanitarian Mine Action has generated extensive best-practice knowledge, but much remains locked in unstructured reports. We introduce TextMine, an ontology-guided pipeline that uses Large Language Models to extract knowledge triples from HMA texts. TextMine integrates document chunking, domain-aware prompting, triple extraction, and both reference-based and LLM-as-a-Judge evaluation. We also create the first HMA ontology and a curated dataset of real-world demining reports. Experiments show ontology-aligned prompts boost extraction accuracy by 44.2%, cut hallucinations by 22.5%, and improve format conformance by 20.9% over baselines. While validated on Cambodian reports, TextMine can adapt to global demining efforts or other domains, transforming unstructured data into structured knowledge.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: NLP for social good

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 648