Structuring Risk Factors of Industrial Incidents Using Natural Language Process 


Vol. 36,  No. 1, pp. 56-63, Feb.  2021
10.14346/JKOSOS.2021.36.1.56


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  Abstract

The narrative texts of industrial accident reports help to identify accident risk factors. They relate the accident triggers to the sequence of events and the outcomes of an accident. Particularly, a set of related keywords in the context of the narrative can represent how the accident proceeded. Previous studies on text analytics for structuring accident reports have been limited to extracting individual keywords without context. We proposed a context-based analysis using a Natural Language Processing (NLP) algorithm to remedy this shortcoming. This study aims to apply Word2Vec of the NLP algorithm to extract adjacent keywords, known as word embedding, conducted by the neural network algorithm based on supervised learning. During processing, Word2Vec is conducted by adjacent keywords in narrative texts as inputs to achieve its supervised learning; keyword weights emerge as the vectors representing the degree of neighboring among keywords. Similar keyword weights mean that the keywords are closely arranged within sentences in the narrative text. Consequently, a set of keywords that have similar weights presents similar accidents. We extracted ten accident processes containing related keywords and used them to understand the risk factors determining how an accident proceeds. This information helps identify how a checklist for an accident report should be structured.

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[IEEE Style]

강성식, 장성록, 이종빈, 서용윤, "Structuring Risk Factors of Industrial Incidents Using Natural Language Process," Journal of the Korean Society of Safety, vol. 36, no. 1, pp. 56-63, 2021. DOI: 10.14346/JKOSOS.2021.36.1.56.

[ACM Style]

강성식, 장성록, 이종빈, and 서용윤. 2021. Structuring Risk Factors of Industrial Incidents Using Natural Language Process. Journal of the Korean Society of Safety, 36, 1, (2021), 56-63. DOI: 10.14346/JKOSOS.2021.36.1.56.

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