Title: Extracting typical incident patterns from text data

Authors: Toru Nakata

Addresses: Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Aomi 2-4-7, Koto-ku, Tokyo, 135-0064, Japan

Abstract: To prevent industrial incidents, it is important to learn why and how past incidents occurred and escalated. Information regarding accidents is recorded primarily in natural language texts, which are not convenient for analysing incident progression. This paper proposes a method for recognising the typical flow of events in a large set of text reports. Our method transforms each sentence in reports about industrial incidents into a vector (bag-of-words) to facilitate the detection of similar contexts and stories. In this way, we can recognise the typical progression of accidents.

Keywords: incident analysis; text mining; bag of words; BoW; modelling; safety engineering.

DOI: 10.1504/IJHFMS.2018.093177

International Journal of Human Factors Modelling and Simulation, 2018 Vol.6 No.2/3, pp.127 - 139

Received: 04 Sep 2017
Accepted: 13 Dec 2017

Published online: 29 Jun 2018 *

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