RECKONition: a NLP-based system for Industrial Accidents at Work Prevention
This work addresses industrial accident prevention for Italian stakeholders, but appears incremental as it applies existing NLP methods to a new language-specific dataset.
The authors tackled the challenge of extracting patterns from non-English (Italian) textual data on industrial accidents, and developed RECKONition, an NLP-based system that successfully processed Italian accident descriptions to aid in prevention.
Extracting patterns and useful information from Natural Language datasets is a challenging task, especially when dealing with data written in a language different from English, like Italian. Machine and Deep Learning, together with Natural Language Processing (NLP) techniques have widely spread and improved lately, providing a plethora of useful methods to address both Supervised and Unsupervised problems on textual information. We propose RECKONition, a NLP-based system for Industrial Accidents at Work Prevention. RECKONition, which is meant to provide Natural Language Understanding, Clustering and Inference, is the result of a joint partnership with the Italian National Institute for Insurance against Accidents at Work (INAIL). The obtained results showed the ability to process textual data written in Italian describing industrial accidents dynamics and consequences.