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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Adapting natural language processing for technical text.

Alden Dima1, Sarah Lukens2, Melinda Hodkiewicz3

  • 1Information Technology Laboratory, National Institute of Standards and Technology, Maryland, USA.

Applied AI Letters
|April 14, 2023
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Summary
This summary is machine-generated.

Natural Language Processing (NLP) struggles with real-world technical data. Technical Language Processing (TLP) applies engineering to extract actionable information from expert language, improving industrial maintenance.

Keywords:
domain adaptationmaintenance recordsnatural language processingtechnical datatechnical language processing

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Engineering

Background:

  • Current Natural Language Processing (NLP) methods face limitations in real-world applications due to reliance on standard corpora and computational demands.
  • Domain-specific adaptation of NLP is challenging, hindering the extraction of valuable insights from technical language.

Purpose of the Study:

  • Propose Technical Language Processing (TLP) as an engineering-focused approach to NLP.
  • Enable extraction of actionable information from expert-generated technical language.
  • Address challenges in data quantity and quality within engineering domains.

Main Methods:

  • Envisage NLP as a socio-technical system, moving beyond algorithmic pipelines.
  • Develop TLP principles integrating engineering practices into NLP.
  • Illustrate TLP through a case study in industrial maintenance.

Main Results:

  • TLP offers a novel approach to meaning and generalization distinct from traditional NLP.
  • Provides strategies for managing data quantity and quality in technical fields.
  • Demonstrates potential benefits for engineering problems through unstructured data integration.

Conclusions:

  • Adapting NLP for technical use cases is crucial to unlock insights from unstructured expert data.
  • TLP can significantly enhance problem-solving in engineering domains, particularly in areas like industrial maintenance.
  • Technical Language Processing represents a necessary evolution for applying AI to specialized technical language.