Extraction and Visualization of Occupational Health and Safety Related Information from Open Web

Abstract

In this paper, we have proposed natural language processing and deep learning based techniques for the automatic extraction and curation of occupational health and safety related information from safety-related articles. Such articles typically contain details of the organizations that have been cited for violating the health and safety regulations, safety-related issues and incidents, the location of the incident, and finally details of the penalties incurred. We have done experiments with a collection of 5400 related articles. The end-product of our work is an occupational risk-register that contains details of safety incidents across geographies and time. This register can be further utilized for analytical and reporting purposes. Such information is extremely valuable to industries which see a high occurrence of occupational injuries.