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.- Anthology ID:
- 2022.wnut-1.16
- Volume:
- Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 147–153
- Language:
- URL:
- https://aclanthology.org/2022.wnut-1.16
- DOI:
- Cite (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.
- Cite (Informal):
- Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports (Dimeski & Rahimi, WNUT 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.wnut-1.16.pdf
- Code
- adamdimeski/automatic-extraction-of-mining-company-drillhole-results