Natural Language Processing (NLP)

Natural language processing is the field of artificial intelligence concerned with giving computers the ability to understand and work with human language — text and speech — in ways that are actually useful.

Every time you use autocomplete on your phone, ask a voice assistant a question, run a Google search, or chat with an AI assistant, you are using NLP technology.

Why language is hard for computers

Language seems easy to us because we have been doing it since childhood, but it is extraordinarily complex for machines. Consider the challenges:

• Ambiguity: "I saw her duck" — did she duck down, or did I see a duck that belongs to her?
• Context: "It's cold" means different things said in a freezer, at a meeting, or about a relationship.
• Idiom: "Break a leg" does not mean what the individual words suggest.
• Sarcasm: "Oh great, another meeting" — the words say positive, the meaning is negative.
• Implication: "Do you know what time it is?" is usually not asking for information but expressing frustration about lateness.

NLP research has spent decades solving these problems, and the arrival of transformer-based language models has dramatically improved performance across all of them.

Core NLP tasks

NLP covers a wide range of capabilities:

• Text classification: Categorising text — is this email spam? Is this review positive or negative? Is this support ticket urgent?
• Named entity recognition: Identifying specific things in text — people, companies, dates, locations, products.
• Sentiment analysis: Determining the emotional tone of text — useful for analysing customer feedback, social media, and surveys.
• Summarisation: Condensing long documents into shorter summaries while preserving key information.
• Translation: Converting text from one language to another.
• Question answering: Extracting specific answers from a body of text.
• Text generation: Producing new text — from email drafts to marketing copy to code.

NLP before and after LLMs

Before large language models, each NLP task required its own specialised model. You needed one model for sentiment analysis, another for translation, another for summarisation. Building each model required labelled training data specific to that task.

LLMs changed this fundamentally. A single large language model can perform all of these tasks and more, simply by changing the prompt. This is why LLMs are sometimes called foundation models — they serve as the foundation for dozens of applications.

NLP in business today

Practical business applications of NLP include:

• Customer service chatbots that understand and respond to queries
• Automated email categorisation and routing
• Contract analysis and key clause extraction
• Meeting transcription and summary generation
• Social media monitoring and brand sentiment tracking
• Multilingual content creation and localisation