NLP Basics: How Machines Understand Language
Explore the fundamentals of Natural Language Processing — tokenization, embeddings, transformers, attention mechanisms, and how AI understands human language. Natural Language Processing (NLP) is the branch of AI that enables computers to understand, interpret, and generate human language. From chatbots and translation tools to sentiment analysis and text summarization, NLP powers many of the AI applications we use daily. What is NLP and Why Is It Hard? Human language is incredibly complex. Words have multiple meanings depending on context, sentences can be ambiguous, sarcasm and humor are difficult to detect, and new words and phrases emerge constantly. NLP tackles these challenges by combining linguistics with machine learning. Early NLP relied on hand-written rules, but modern NLP uses deep learning to learn language patterns directly from massive text datasets. This shift has led to dramatic improvements in language understanding and generation. Tokenization Tokenization is the first step in NLP. It breaks text into smaller pieces called tokens, which can be words, subwords, or characters. For example, the sentence "I love AI tools" might be tokenized as ["I", "love", "AI", "tools"]. Modern tokenizers use subword tokenization (like Byte-Pair Encoding or WordPiece), which handles rare words by breaking them into known subword units. This means "unhappiness" might be tokenized as ["un", "happiness"], allowing the model to understand the meaning even if it hasn't seen the exact word.