An interactive machine-learning method to obtain safety information from free text

Peter Hughes, Coen Van Gulijk, Rawia El Rashidy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes the continued development of natural language processing (NLP) techniques to support safety management on the GB railways. The work considers machine reading techniques to obtain information from more than 800,000 free text hazard records in the Close Call System (CCS) Our research has found that the non-standard nature of the text means that standard NLP techniques yield accuracies between 0% and 60% when categorising hazard reports. To improve accuracy, a workflow was developed that uses the results from individual techniques in an iterative cycle with a human analyst. The technique, dubbed interactive learning, has achieved substantially improved accuracy of up to 98% and is currently being integrated by the GB railway industry as part of its SMS.
Original languageEnglish
Title of host publicationProceedings of the 29th European Safety and Reliability Conference (ESREL 2019)
EditorsMichael Beer, Enrico Zio
Pages46-54
Number of pages9
ISBN (Electronic)9789811127243
Publication statusPublished - Sep 2019
Event29th European Safety and Reliability Conference - Leibniz Universität, Hannover, Germany
Duration: 22 Sep 201926 Sep 2019
Conference number: 29
https://esrel2019.org/#/

Conference

Conference29th European Safety and Reliability Conference
Abbreviated titleESREL 2019
CountryGermany
CityHannover
Period22/09/1926/09/19
Internet address

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  • Cite this

    Hughes, P., Van Gulijk, C., & El Rashidy, R. (2019). An interactive machine-learning method to obtain safety information from free text. In M. Beer, & E. Zio (Eds.), Proceedings of the 29th European Safety and Reliability Conference (ESREL 2019) (pp. 46-54)