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        <title>Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports
%A Dimeski, Adam
%A Rahimi, Afshin
%S Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F dimeski-rahimi-2022-automatic
%X 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.
%U https://aclanthology.org/2022.wnut-1.16
%P 147-153

Markdown (Informal)

[Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports](https://aclanthology.org/2022.wnut-1.16) (Dimeski & Rahimi, WNUT 2022)

• Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports (Dimeski & Rahimi, WNUT 2022)

ACL

• Adam Dimeski and Afshin Rahimi. 2022. Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 147–153, Gyeongju, Republic of Korea. Association for Computational Linguistics.