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Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine
- Source :
- Minerals, Volume 11, Issue 7, Minerals, Vol 11, Iss 776, p 776 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data.
- Subjects :
- Computer science
0211 other engineering and technologies
accidents
02 engineering and technology
010501 environmental sciences
computer.software_genre
Machine learning
01 natural sciences
Mine site
random forest classification
mine safety and health
narratives
021105 building & construction
Narrative
natural language processing
0105 earth and related environmental sciences
Single model
business.industry
Geology
Mineralogy
Geotechnical Engineering and Engineering Geology
Random forest
machine learning
Management system
Artificial intelligence
business
computer
Natural language processing
Mine safety
QE351-399.2
Subjects
Details
- Language :
- English
- ISSN :
- 2075163X
- Database :
- OpenAIRE
- Journal :
- Minerals
- Accession number :
- edsair.doi.dedup.....c542b577a296735270aaf41ef89d132a
- Full Text :
- https://doi.org/10.3390/min11070776