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Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of engineering data. Text-based documents account for a significant portion of data collected during the life cycle of asset management and the valuable information these documents contain is underutilized in analysis. Meanwhile, researchers historically design NLP pipelines with non-technical language in mind. This means industrial implementations are built on tools intended for non-technical use cases, suffering from a lack of verification, validation, and ultimately, personnel trust. To mitigate these sources of risk, we encourage a holistic, domain-driven approach to using NLP in a technical engineering setting, a paradigm we refer to as Technical Language Processing (TLP). Toward this end, the industrial asset management community must collectively redouble efforts toward production of and consensus around key domain-specific resources, including: (1) goal-driven data representations, (2) flexible entity type definitions and dictionaries, and (3) improved access to data-sets – raw and annotated. This collective action allows the maintenance community to follow in the path of other scientific communities, e.g., medicine, to develop and utilize these public resources to make TLP a key contributor to Industry 4.0.
Brundage et al. (Fri,) studied this question.