What is Natural Language Processing?

Natural Language Processing is the technical discipline that teaches machines to work with human language — a task that is deceptively complex. Human language is ambiguous, context-dependent, full of idiom and implication, and constantly evolving. NLP algorithms process text (and increasingly, spoken audio) to extract meaning, intent, structure, and sentiment from what is inherently unstructured data.

Core NLP tasks include: text classification (categorizing text by topic, intent, or sentiment — e.g., spam detection, customer sentiment analysis); named entity recognition (identifying people, places, organizations, and dates in text); machine translation (converting text between languages); question answering (extracting answers to questions from a body of text); summarization (condensing long documents into key points); and text generation (producing coherent human-like text — the core capability of large language models).

Modern NLP is dominated by Transformer-based models — the same architecture underlying LLMs like GPT-4 and Claude. Pre-trained models that are then fine-tuned on specific tasks have replaced the specialized, task-specific models that previously required separate development. This means the same foundational model can be applied to sentiment analysis, translation, and text generation with only task-specific fine-tuning required.