AI & Automation
Natural Language Processing
Definition
Natural language processing (NLP) is the field of artificial intelligence concerned with enabling computers to understand, interpret, and generate human language.
What is natural language processing?
Natural language processing (NLP) is the branch of artificial intelligence concerned with the interaction between computers and human language. Its aim is to let software read, interpret, and produce language the way people naturally use it, in all its ambiguity, variety, and imperfection. (The term is sometimes written "natural-language processing" with a hyphen; both refer to the same field.)
NLP combines linguistics with machine learning. Rather than being told explicit rules for every phrase, modern NLP systems learn patterns from large amounts of real text, which is what allows them to handle slang, typos, and the many different ways people express the same request.
Common NLP techniques
NLP is a broad field made up of many complementary techniques, including:
- Intent recognition. Working out what a person is trying to achieve. See intent recognition.
- Entity extraction. Pulling out specific details such as names, dates, order numbers, or products. See entity extraction.
- Sentiment analysis. Judging the emotional tone of a message, from positive to negative. See sentiment analysis.
- Tokenisation and parsing. Breaking text into units and analysing its grammatical structure.
- Language generation. Producing fluent, natural responses rather than only interpreting input.
Most real applications chain several of these together, so a single message can be classified, understood, and acted on in one pass.
NLP in customer support
In customer service, NLP is what lets automated systems understand a request written in ordinary language. It powers the understanding behind chatbots, directs incoming messages to the right place through routing, and reads sentiment so urgent or unhappy messages can be prioritised. Applied across a whole inbox, it also turns unstructured conversations into insight, revealing which issues come up most often and how customers feel about them.
NLP and large language models are closely related but not the same: NLP is the broad field, while a large language model is one especially capable technology now used to perform many NLP tasks at once. An LLM is, in effect, a powerful engine for the kind of language understanding NLP has always aimed at.
Why it matters
Example
A support inbox receives, 'my payment won't go through and I'm getting a bit frustrated.' NLP extracts the intent, a failed payment, identifies the entity, the payment method, and reads the sentiment as negative. That structured understanding lets the system route the message correctly, prioritise it, and prompt the right response, all from a single line of ordinary text.
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Related terms
Frequently asked questions
What is natural language processing?
Natural language processing, or NLP, is the field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. It combines linguistics with machine learning so software can work with everyday language rather than rigid commands.
What are common natural language processing techniques?
Widely used techniques include intent recognition, entity extraction, sentiment analysis, tokenisation, parsing, and language generation. Real applications usually combine several, so a message can be understood and acted on in one pass.
How is NLP used in customer support?
NLP lets support systems understand requests written in plain language, powering chatbots, routing messages to the right team, and reading sentiment to prioritise urgent cases. Across a whole inbox it also surfaces common issues and trends.
What is the difference between NLP and a large language model?
NLP is the broad field concerned with computers and human language, while a large language model is one powerful technology now used to perform many NLP tasks. In short, an LLM is a tool used within the wider discipline of NLP.