Natural Language Processing (NLP) allows computers to generate, understand, and effectively communicate with humans through language.
NLP allows for voice and language applications and significantly improves the transmission of information between humans and machines. It can also help human-to-human communications through translation tools that can accurately capture the meaning and tones of the speaker’s words.
In this article, I’ll walk you through the world of Natural Language Processing.
I’ll provide everything you need to get an intuitive understanding of the technology, and how you’re likely to use it.
What is Natural Language Processing?
Natural Language Processing (NLP) is a subfield of Artificial Intelligence and computational linguistics focused on enabling computers to understand, interpret, and generate human language.
NLP combines computational linguistics, machine learning, and deep learning techniques to analyze and synthesize text and speech.
Natural Language Processing is used in various applications like voice assistants, chatbots, sentiment analysis, and machine translation, bridging the gap between human communication and computer understanding.
There are countless books on artificial intelligence and natural language processing if you’re after a deep-dive into the topics.
The History of Natural Language Processing
John McCarthy, a computer scientist who is credited with the term “artificial intelligence,” plays chess against a computer (1966) – Chuck Painter
Natural language processing was first seen in the mid-1950s as early computer linguistics and machine learning methods began to appear. In January 1954, the Georgetown-IBM experiment had a computer translate more than 60 Russian sentences into English, and its authors reckoned machine translation could be a solved problem within three to five years. It took a bit longer than that.
The Turing Test set the foundation for machines to interpret and generate human languages by establishing the principle that a machine’s intelligence could be measured by its ability to produce indistinguishable human-like responses in a conversation.
This idea of machine intelligence guided the early directions of NLP and brought about a new era, one where machines could communicate with humans.
From there, NLP moved from hand-written rules to statistical methods, then to deep learning, and in 2017 to the transformer, the architecture behind today's large language models. When OpenAI released ChatGPT in November 2022, NLP went mainstream almost overnight.
Core Components of NLP
Natural Language Processing is a broad field encompassing several sub-disciplines: Natural Language Understanding (NLU), and Natural Language Generation (NLG).
While NLP is the overarching field that allows machines to understand, process, and generate human language, natural language understanding, and natural language generation are specialized components of this process.
NLU involves interpreting the meanings of human language (understanding what you say) while NLG is about generating meaningful phrases and sentences in a human-like manner (responding with something you’ll understand).
Together, these components enable interactive and intelligent systems that can engage with humans in a meaningful way. We’ll take an individual look at NLU and NLG later in the article.
Natural Language Understanding (NLU)
Natural Language Understanding goes beyond merely processing language. NLU aims to comprehend the intricacies and nuances of language, tackling ambiguities and understanding context.
NLU considers two main aspects of human language:
- Semantics: Semantic analysis describes the process of interpreting the true meaning of words, phrases, and sentences in context. Semantic analysis is about understanding the direct and literal meanings of text.
- Pragmatics: Pragmatics deals with the understanding of language in context, deciphering implied meanings that depend on the situation, speaker’s intent, cultural aspects, and other factors beyond the literal content of the language.
You can think of NLU as the part that enables computers to understand thick accents and slang words.
By breaking down language in this manner, NLU gives computers a much greater understanding of the nuances of human dialect.
Natural Language Generation (NLG)
Natural Language Generation is the process that allows computers to create human-like responses from data.
This field of NLP is what allows the AI to ‘back and forth’, effectively sustaining a conversation with the user.
NLG uses statistical methods and language models to analyze large amounts of text data to predict what text comes next, allowing it to generate sentences that make sense.
NLG is used to summarize text, translate languages, and even generate creative content like stories or poems, all of which we’ve seen implemented with tools like ChatGPT many times over.
Techniques of Natural Language Processing
NLP involves several techniques that help computers understand and analyze language effectively. Let’s take a closer look at some of the fundamentals:
- Tokenization: Tokenization breaks text into individual words or tokens, making it easier for deep learning models to process the text. Large language models usually split text into sub-word tokens, so a long or unusual word might become several tokens.
- Part-of-Speech Tagging: Part of Speech tagging assigns grammatical roles to these tokens such as whether it’s a noun, verb, adjective, etc.
- Parsing: Parsing involves analyzing the grammatical structure of a sentence. It helps a computer understand the relationship between different words.
- Named Entity Recognition: Named Entity Recognition (NER) scans the text to identify and categorize key information like names and places. It’s effectively capturing the who, where, and when of a text.
- Co-reference Resolution: Co-reference resolution helps the computer understand the context by linking pronouns or abbreviated terms back to the original entities they refer to. For example, in the sentence “Sara said she would visit the park,” the term “she” refers back to “Sara.”
- Sentiment Analysis: Sentiment Analysis involves determining the underlying sentiment or emotion of a piece of text, whether it’s positive, negative, or neutral.
- Word Sense Disambiguation: Word sense disambiguation is essential for machines to grasp the nuanced meanings of words based on the context in which they appear. For instance, the word “bank” could refer to a financial institution or the side of a river. Word sense disambiguation algorithms help the machine determine which meaning is relevant in a given situation.
Many of the top machine learning newsletters talk about the latest developments in NLP and are certainly worth a look if you’re interested in the space!
Popular NLP Algorithms and Models
Machine Learning in NLP
Machine learning serves as the backbone for natural language models, employing algorithms to learn from large datasets.
Techniques like Naive Bayes, Decision Trees, and Support Vector Machines are commonly used for text classification, sentiment analysis, and language modeling.
These algorithms enable NLP systems to recognize patterns, understand context, and make predictions or decisions based on the data.
Deep Learning in NLP
Deep learning models take NLP a step further by using neural networks to analyze complex language data.
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks were the popular choice for sequence modeling tasks like machine translation and speech recognition through the 2010s.
These models can capture long-term dependencies and nuances in language, making them highly effective for NLP. I've explained how they work in my guide to the types of neural networks.
Popular Algorithms
Popular algorithms in NLP include the Viterbi algorithm for part-of-speech tagging and Hidden Markov Models for sequence prediction.
These algorithms are the building blocks that power various NLP applications, contributing to the field’s rapid advancements.
Transformers and Large Language Models
Transformer models like BERT and GPT changed everything. Instead of reading text one word at a time, a transformer looks at a whole passage at once and works out which words matter most to each other.
Scale that up with enormous amounts of text and you get large language models (LLMs) like OpenAI's GPT models (the brains behind ChatGPT), Google's Gemini, Anthropic's Claude and Meta's Llama. These now dominate NLP. A single LLM can translate, summarize, answer questions, classify sentiment and write code, jobs that each used to need their own specially trained model.
Real World Applications of NLP
Natural Language Processing has wide-reaching applications, many of these NLP tasks are becoming increasingly common in our daily lives:
- Education: NLP is being utilized in intelligent tutoring systems to provide personalized instruction and feedback to students.
- Healthcare: NLP powers AI medical scribes, which listen to a doctor's consult and write the clinical note.
- Law: NLP aids in extracting insights from complex legal documents or analyzing surveillance transcripts.
- Finance: NLP is employed in the finance sector for risk analysis and fraud detection by reading news, reports and customer messages for warning signs. It can also be employed for stock trading applications.
- Media: NLP can be used with speech recognition to generate automatic subtitles or assist in machine translation, enhancing accessibility for a global audience.
- Marketing: NLP-driven recommendation systems analyze customer reviews and preferences to suggest personalized products or services.
- Recruitment: NLP is increasingly being applied to screen and shortlist job candidates.
- Fitness: NLP can be employed to generate customized health and fitness plans by using existing workouts and information about the trainee.
The list of NLP tasks is far from exhaustive. The applications of NLP extend far and wide, its capabilities reshaping industries across the board.
Case Studies Using NLP
ELIZA
Back in the 1960s, ELIZA was one of the first programs to mimic a conversation using computational linguistics. While she might not have truly ‘understood’ human language, she used simple pattern-matching rules to respond to user input.
ELIZA was a simple if-then rules-based system. This meant that ELIZA had a pre-defined set of responses to certain user inputs, similar to the old-school scripted customer service chatbots you still bump into today. Modern AI chatbots have moved on a long way: here are the best AI chatbots for small business in Australia if you want to see what they can do now.
Although the technology is considered basic now, ELIZA was a pioneering force for NLP and computer science.
She helped to showcase the potential of machines to communicate with humans and truly was the grandmother of modern natural language processing.
Alexa
Amazon’s virtual assistant Alexa is a perfect demonstration of NLP, NLU, and NLG in action.
When you ask Alexa a question, NLP helps break down your speech into understandable units, NLU interprets your intent and context, and NLG generates an appropriate and coherent response.
This complex process happens in mere seconds thanks to the powerful technology that is natural language processing!
Amazon has since rebuilt Alexa around large language models. The new version, Alexa+, was unveiled in February 2025 and has been rolling out country by country, reaching Australia in early access in August 2026.
Fighting Fraud
Have you ever received a call from your bank regarding suspicious activity on your account? Those alerts usually come from machine learning models watching your transactions, and NLP is a growing part of the fraud-fighting toolkit. Insurers, for example, use NLP to comb through claims and adjusters' notes for the shared phrases and accident stories that often point to organized fraud.
Mastercard uses an AI system called Decision Intelligence. This tool analyzes cardholder behaviors, locations, and purchasing patterns, identifying any unusual transactions that may indicate fraudulent activity.
In 2024, Mastercard added generative AI to it, which the company says lifts fraud detection rates by 20% on average, helping it protect its customers and secure their trust.
Strictly speaking, that's machine learning on transaction data rather than language, but it's the same pattern-spotting idea that powers NLP, pointed at a different kind of data.
The Future of NLP
As advances in AI continue and NLP becomes more widely adopted, we can expect to see NLP making an even greater impact in our everyday lives.
When I first wrote this, I said to imagine having a real-time conversation with someone in another language, generating a bedtime story for your child on the spot based on something that happened in their day, or real-time AI sports commentating.
All three are now a thing. Apple's AirPods can translate face-to-face conversations live, Google's newer Pixel phones can translate phone calls in something close to your own voice, any decent chatbot will knock out a bedtime story in seconds, and IBM started adding AI-generated commentary to Wimbledon highlight videos back in 2023.
The next frontier is models that mix language with vision and audio, so an AI can look at what your camera sees and talk you through it. That's where NLP meets computer vision.
These advancements will improve the way we can interact with the digital world more and more as they advance and serve to fundamentally change the way humans and machines interact.
Final Thoughts
Natural Language Processing is a transformative technology allowing us to better interact with machines and the digital world, as well as one another.
NLP already underpins everyday technologies like voice-enabled assistants and conversational AIs. NLP will undoubtedly go on to influence countless other sectors as AI continues to evolve.
The potential of natural language processing is limitless and personally I find it very exciting to be witnessing the progression of such a world-changing technology in real-time.
Well, that’s all from me. Hope you enjoyed this deep dive into NLP!
