@inproceedings{dimeski-rahimi-2022-automatic,
    title = "Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports",
    author = "Dimeski, Adam  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.wnut-1.16/",
    pages = "147--153",
    abstract = "Aggregate mining exploration results can help companies and governments to optimise and police mining permits and operations, a necessity for transition to a renewable energy future, however, these results are buried in unstructured text. We present a novel dataset from 23 Australian mining company reports, framing the extraction of structured drillhole information as a sequence labelling task. Our two benchmark models based on Bi-LSTM-CRF and BERT, show their effectiveness in this task with a F1 score of 77{\%} and 87{\%}, respectively. Our dataset and benchmarks are accessible online."
}