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Updated: May 26, 2026
16:05
Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools
Published on: October 1, 2007
Technical Language Processing: Unlocking Maintenance Knowledge
Michael P Brundage1, Thurston Sexton1, Melinda Hodkiewicz2
1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
Manufacturing Letters
|May 25, 2026
Summary
Natural Language Processing (NLP) requires adaptation for engineering data, necessitating a domain-driven approach called Technical Language Processing (TLP). Developing shared resources for TLP is crucial for industrial asset management and Industry 4.0 integration.
Area of Science:
- Engineering Data Analysis
- Natural Language Processing
- Industrial Asset Management
Background:
- Text-based documents are underutilized in asset management despite containing valuable data.
- Existing Natural Language Processing (NLP) pipelines are designed for non-technical language, leading to trust and validation issues in industrial settings.
Purpose of the Study:
- To propose a domain-driven approach, Technical Language Processing (TLP), for analyzing engineering data.
- To highlight the need for specialized resources to improve NLP in technical contexts.
Main Methods:
- Advocating for a holistic, domain-driven approach to NLP in engineering.
- Encouraging the development of domain-specific resources for Technical Language Processing (TLP).
Main Results:
- Identified limitations of standard NLP for engineering data, impacting verification, validation, and trust.
- Proposed TLP as a paradigm to address these limitations.
Conclusions:
- The industrial asset management community needs to collaborate on creating domain-specific resources like data representations, entity definitions, and accessible datasets.
- Adoption of these resources will enable Technical Language Processing (TLP) to significantly contribute to Industry 4.0 initiatives.
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