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Safety 2018, 4(3), 30; https://doi.org/10.3390/safety4030030

Multi-Label Classification of Contributing Causal Factors in Self-Reported Safety Narratives

Parks College of Engineering, Aviation and Technology, Saint Louis University, Saint Louis, MO 63103, USA
Received: 13 March 2018 / Revised: 21 April 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
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Abstract

Three methods are demonstrated for automated classification of aviation safety narratives within an existing complex taxonomy. Utilizing latent semantic analysis trained against 4497 narratives at the sentence level, primary problem and contributing factor labels were assessed. Results from a sample of 2987 narratives provided a mean unsupervised categorization precision of 0.35% and recall of 0.78% for contributing-factors within the taxonomy. Categorization of the primary problem at the sentence level resulted in a modal accuracy of 0.46%. Overall, the results suggested that the demonstrated approaches were viable in bringing additional tools and insights to safety researchers. View Full-Text
Keywords: latent semantic analysis (LSA); adaptive taxonomy; safety; automatic indexing; machine learning latent semantic analysis (LSA); adaptive taxonomy; safety; automatic indexing; machine learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Robinson, S.D. Multi-Label Classification of Contributing Causal Factors in Self-Reported Safety Narratives. Safety 2018, 4, 30.

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