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Technical Language Processing: Unlocking Maintenance Knowledge

Published

Author(s)

Michael P. Brundage, Thurston B. Sexton, Melinda Hodkiewicz, Alden A. Dima, Sarah Lukens

Abstract

Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of the engineering sector. 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. Toward this end, the industrial asset management community community must redouble efforts toward production of (and consensus around) key domain-specific resources. These include 1) goal-driven data representations and feature engineering, 2) flexible entity type definitions and dictionaries, and 3) improved access to data-sets -- raw and annotated.
Citation
Manufacturing Letters

Keywords

maintenance, natural language processing, technical language processing

Citation

Brundage, M. , Sexton, T. , Hodkiewicz, M. , Dima, A. and Lukens, S. (2020), Technical Language Processing: Unlocking Maintenance Knowledge, Manufacturing Letters, [online], https://doi.org/10.1016/j.mfglet.2020.11.001 (Accessed September 23, 2021)
Created December 10, 2020, Updated December 14, 2020