- Natural language processing in plain English
- How natural language processing works step by step
- What natural language processing can do for a small business
- Understanding versus generating in natural language processing
- Where natural language processing struggles
- Natural language processing tools you may already be using
- Natural language processing, machine learning and AI: how they fit
- Frequently asked questions
Natural language processing (NLP) is the technology that lets computers read, interpret and produce human language, such as emails, reviews and speech. It combines linguistics with machine learning to sort, translate, summarise and answer text. The part people trip over: it handles patterns in language well, but sarcasm and double meanings can still fool it.
Every time you ask a chatbot a question, dictate a message or watch your phone suggest the next word, natural language processing is doing the quiet work. I find most business owners use it daily without ever hearing the name. Once you know what it does, it's easier to pick sensible tools and to spot where they'll let you down.
Natural language processing in plain English
AWS describes natural language processing, or NLP, as technology that allows computers to interpret, manipulate and comprehend human language. It sits where computer science, linguistics and machine learning meet.
The simplest way to picture it: people write and speak in messy, loose, everyday language, while computers want tidy, structured data. NLP is the translator in the middle. It turns a rambling customer email into something a system can sort, count and respond to.
How natural language processing works step by step
AWS describes a typical NLP project as a series of stages. Text or speech is gathered and prepared, then pre-processed (broken into pieces, with common filler words removed and word endings simplified), then used to train a model, which is finally put to work on new text.
Wikipedia traces how the methods changed over the decades:
- Hand-written rules. Until the 1980s, most systems were based on complex sets of rules written by people.
- Statistics. Machine learning then replaced many of those rules, learning patterns from large collections of text.
- Neural networks. Wikipedia says that since about 2015 neural methods have increasingly replaced the older statistical approaches.
The famous early milestone is the Georgetown-IBM experiment in 1954, which AWS says automatically translated 60 Russian sentences into English. Seventy years on, the idea is the same but the tools are far stronger. If you'd like the building blocks behind the newest versions, my plain guide to where ChatGPT gets its information shows how today's chat assistants draw on text.
What natural language processing can do for a small business
Wikipedia and AWS between them list the main jobs. Here's the list, with everyday examples:
| NLP task | Plain meaning | Everyday business example |
|---|---|---|
| Sentiment analysis | Is the tone positive, negative or neutral? | Sorting a pile of customer reviews |
| Text classification | Putting text into categories | Routing enquiries to sales or support |
| Machine translation | Converting between languages | Reading a supplier's message in another language |
| Speech recognition | Turning spoken words into text | Transcribing a client call |
| Named entity recognition | Pulling out names, places, dates | Finding order numbers in emails |
| Summarization | Shortening long text | A quick summary of a long report |
| Question answering | Replying to a direct question | A help chatbot on your website |
AWS also gives real examples from other companies: one uses Amazon Comprehend to pull policy numbers out of insurance documents, and another spots keywords in customer text messages to offer personalised recommendations. Neither involves anything exotic, just a lot of reading done at speed.
Understanding versus generating in natural language processing
Two terms come up constantly and they're easy to mix up.
- Natural language understanding is about reading. Wikipedia describes it as turning text into formal representations that a program can work with.
- Natural language generation is about writing. It turns information or an intended meaning into readable text.
A chatbot that answers a customer does both: it works out what the question means, then it writes a reply. Neural networks are now the main method behind much of this, which is why NLP and tools such as ChatGPT are so often mentioned together. NLP is the wider field, and a chat assistant is one product built with it.
Where natural language processing struggles
Human language is slippery, and AWS says so plainly. Sarcasm, metaphors, varied sentence structures and grammar exceptions all take people years to learn, so they're hard for software too. Its example is the word "bat", which could be an animal or something you hit a ball with. Speech adds accents and unusual intonation on top.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
Wikipedia adds a point that matters if you work in a smaller language: machine learning needs enough training data, so rule-based systems are still used for some low-resource languages. Results can be noticeably weaker outside the biggest languages.
That leads to a few sensible habits:
- Read a sample of what the tool produces before you rely on it.
- Don't treat a sentiment score as a verdict on a real customer.
- Keep a person in the loop for complaints, legal wording and anything sensitive.
- Check where your text is sent and kept, using the tool's own privacy page, before you feed it customer messages.
Natural language processing tools you may already be using
You probably don't need to build anything. NLP is already built into everyday software:
- Email spam filters and inbox sorting.
- Voice assistants and dictation on your phone.
- Translation features in browsers and messaging apps.
- Website chat widgets that answer common questions.
- Review and social listening tools that flag the mood of comments.
When you're ready to go further, my roundup of the best AI tools for small business owners is a good place to see what's on offer, and if you want a hand choosing and setting things up, that's what I do as an AI consultant.
Natural language processing, machine learning and AI: how they fit
AI is the big umbrella. Machine learning is the part of AI that learns from examples. NLP applies machine learning (and the older rule-based methods) to human language specifically. Computer vision does the same job for pictures. Knowing which box a tool belongs in helps you ask better questions when someone sells it to you.
Frequently asked questions
What is natural language processing in simple terms?
It's the technology that lets computers read, interpret and produce human language, whether typed or spoken. Spam filters, voice dictation and chatbots all use it.
What is an example of natural language processing?
A website chatbot that works out what a visitor is asking and replies is a good one. Other everyday examples are voice dictation, translation apps and tools that sort customer reviews by mood.
Is natural language processing the same as ChatGPT?
No. NLP is the wider field of getting computers to handle language. ChatGPT is one product built with modern NLP methods, specifically a large language model.
What are the main tasks in natural language processing?
Wikipedia and AWS list sentiment analysis, text classification, machine translation, speech recognition, named entity recognition, summarization and question answering.
What are the limits of natural language processing?
Sarcasm, metaphors, words with several meanings and accents are all hard, according to AWS. Wikipedia adds that smaller languages with little training data are often weaker.
