Glossary

Natural Language Processing (NLP)

What is Natural Language Processing (NLP)?

Natural language processing is the branch of AI that enables computers to analyse, understand and generate human language in text or speech. It covers tasks such as translation, classification, summarisation, sentiment analysis and question answering. Rules and statistical methods once dominated; today the field is shaped by large language models.

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Topics

NLP
Language
Basics

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Related terms

Natural Language Understanding (NLU)

Natural language understanding is the part of language processing that captures the meaning of an utterance: what intent lies behind it, which people, places or dates are mentioned, and what is its tone? Together with natural language generation (NLG), NLU forms one of the two sides of NLP. One example is a customer service system that recognises “My parcel still hasn't arrived” as a delivery enquiry.

NLP
Intent detection
+1

Natural language generation (NLG)

Natural language generation (NLG) is the area of language processing that produces understandable text from data, bullet points or other inputs. Earlier NLG systems relied on templates and rules; today most use large language models. Typical uses include weather and stock market reports, product descriptions, summaries of key figures and chatbot answers. It is important that figures and facts from the inputs are carried over correctly and unchanged.

language processing
text generation
+1

Template

Large language model (LLM)

A large language model (LLM) is a neural network trained on very large amounts of text, enabling it to understand and generate language. It works by calculating the most likely next token, step by step. LLMs write, summarise, translate, answer questions and generate code; well-known examples include Claude, GPT, Gemini and Llama. Without access to current sources, they only know what was in their training data.

Language models
Generative AI
+1

NLTK

NLTK (Natural Language Toolkit) is an open-source Python library for natural language processing, created at the University of Pennsylvania in the early 2000s. It provides tools for tokenisation, stemming, part-of-speech tagging and simple classification, as well as access to numerous text corpora. NLTK is used mainly in teaching and research; production applications often rely on libraries such as spaCy or on transformer models.

Python
language processing
+1