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Related Experiment Video

Updated: May 26, 2026

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools
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
PubMed
Summary
This summary is machine-generated.

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.

Related Experiment Videos

Last Updated: May 26, 2026

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools
16:05

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Published on: October 1, 2007

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.