Natural Language Processing (NLP)
Core ConceptsThe branch of AI focused on enabling computers to understand, interpret, and generate human language.
NLP is the AI field dedicated to bridging the gap between human language and computer understanding. It encompasses everything from basic tasks (spell checking, language detection) to complex ones (sentiment analysis, machine translation, question answering).
Before LLMs, NLP relied heavily on hand-crafted rules and smaller statistical models. Tasks like named entity recognition, part-of-speech tagging, and sentiment analysis each required specialized models. LLMs have unified many of these tasks — a single model like GPT-4 or Claude can handle virtually all NLP tasks.
Key NLP applications include: machine translation (DeepL, Google Translate), grammar checking (Grammarly, LanguageTool), sentiment analysis, text summarization, content moderation, and conversational AI.
Real-World Example
Grammarly uses NLP to understand not just your grammar but your intent, tone, and audience — then suggests improvements that go beyond simple rule-based corrections.
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FAQ
What is Natural Language Processing (NLP)?
The branch of AI focused on enabling computers to understand, interpret, and generate human language.
How is Natural Language Processing (NLP) used in practice?
Grammarly uses NLP to understand not just your grammar but your intent, tone, and audience — then suggests improvements that go beyond simple rule-based corrections.
What concepts are related to Natural Language Processing (NLP)?
Key related concepts include LLM (Large Language Model), Tokenization, Sentiment Analysis, Chatbot, Conversational AI. Understanding these together gives a more complete picture of how Natural Language Processing (NLP) fits into the AI landscape.