What is Natural Language Processing?
Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables systems to work with human language in written or spoken form. It allows machines to interpret text in a way that reflects
how language is used in real contexts rather than treating words as isolated data points.
In practical terms, NLP makes it possible for systems to engage with language-heavy information such as reports, notes, procedures and narratives. Instead of requiring people to translate that information into rigid fields or codes, NLP allows language to remain natural while still becoming usable by digital systems.
How NLP Interprets Language
NLP interprets language by identifying structure and patterns within text. Rather than reading for meaning the way a person does, it analyzes how language behaves across sentences, documents and collections of content.
This process includes recognizing how words relate to each other, how phrases signal intent and how meaning changes depending on context. Over time, the system builds representations of language that allow it to connect related ideas, even when they are expressed differently. The result is a consistent way to translate language into signals that systems can work with.
What NLP Is Used to Analyze
NLP is applied wherever language carries operational, technical or historical meaning. In
asset-intensive and enterprise environments, this typically involves text that explains what happened, what was observed or how work was performed.
Common sources include:
- Maintenance notes, inspection findings and work execution comments.
- Engineering reports, technical narratives and design justifications.
- Procedures, manuals and operational guidance written for human use.
By working across this material, NLP helps surface meaning that would otherwise remain distributed across thousands of pages and records.
Where NLP Fits in AI Systems
NLP is rarely used on its own. It typically operates as one part of a larger system that combines language interpretation with other forms of analysis.
Within broader AI solutions, NLP often works alongside:
- Retrieval mechanisms that surface relevant documents or records.
- Structured data models that provide asset or system context.
- Reasoning or generation components that synthesize responses.
In this role, NLP serves as the layer that allows AI systems to engage with human language rather than requiring all information to be pre-structured.
[GA1]Can tie LLMs here as the breakthrough in NLP. LLMs have contextual semantic understanding with generative capability and generalization without task specific training.
