What is Natural Language Processing?
NLP is the field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language -- bridging the gap between how humans communicate and how machines process information.
Why is NLP So Important?
Human language is the primary way we communicate knowledge, express ideas, and interact with the world. But language is messy. It is filled with ambiguity, sarcasm, idioms, slang, and context-dependent meaning. The sentence "I saw her duck" could mean you witnessed a person lower their head, or you observed the waterfowl she owns.
NLP tackles this complexity head-on. It gives machines the ability to read documents, understand questions, translate between languages, summarize articles, detect emotions in text, and carry on conversations. Every time you use a search engine, talk to a voice assistant, or get an auto-generated email reply, you are using NLP.
NLP sits at the intersection of computer science, artificial intelligence, and linguistics. It is one of the oldest subfields of AI, with roots going back to the 1950s -- yet it has experienced a dramatic revolution in the past decade thanks to deep learning and the Transformer architecture.
Try It: The NLP Pipeline
NLP systems break language processing into discrete steps. Enter a sentence below and explore what each step does to the text.
Core NLP Tasks
NLP encompasses a wide range of tasks, from basic text processing to sophisticated language understanding and generation.
Tokenization
Breaking text into individual units (words, subwords, or characters) that a model can process.
POS Tagging
Identifying each word's grammatical role: noun, verb, adjective, adverb, etc.
Named Entity Recognition
Detecting and classifying proper nouns: people, organizations, locations, dates, monetary values.
Sentiment Analysis
Determining the emotional tone of text: positive, negative, or neutral.
Machine Translation
Automatically translating text from one language to another (e.g., English to Hindi).
Text Generation
Producing new, coherent text from a prompt -- the core capability of modern LLMs.
Question Answering
Extracting or generating answers to questions from a given context or knowledge base.
Summarization
Condensing long documents into shorter summaries while preserving the key information.
Tokenization: The First Step
Before any NLP model can process text, it must be broken down into tokens. Different tokenization strategies exist.
Word-Level
"Hello world" → ["Hello", "world"]
Splits on spaces and punctuation. Simple but struggles with unknown words (out-of-vocabulary problem).
Subword (BPE/WordPiece)
"unhappiness" → ["un", "happi", "ness"]
Used by GPT, BERT, and Claude. Breaks rare words into common subword pieces. Handles any word, even novel ones.
Character-Level
"cat" → ["c", "a", "t"]
Each character is a token. Maximum flexibility but requires the model to learn spelling from scratch, and sequences become very long.
The Evolution of NLP
NLP has gone through several paradigm shifts, each dramatically improving performance.
Rule-Based Era
Linguists hand-crafted grammar rules and dictionaries. Systems were brittle -- they worked for narrow domains but broke down with real-world language. Example: early machine translation used word-for-word lookup tables.
Statistical Era
Machine learning models learned patterns from large text corpora. N-gram models, Hidden Markov Models, and TF-IDF became standard. Much more robust than rules, but required extensive feature engineering by humans.
Neural / Deep Learning Era
Word2Vec, GloVe, and later ELMo learned rich word representations automatically. RNNs and LSTMs could process sequences. Performance on benchmarks improved dramatically with minimal feature engineering.
Transformer / LLM Era
BERT, GPT, and their successors (Claude, Gemini, LLaMA) use the Transformer architecture. Pre-trained on massive datasets, they achieve human-level or superhuman performance on many NLP tasks. A single model can handle dozens of tasks with zero or few examples.
NLP in the Real World
Search Engines
Google, Bing, and other search engines use NLP to understand query intent, match relevant documents, and generate featured snippets. BERT was first deployed to improve Google Search in 2019.
Voice Assistants
Siri, Alexa, and Google Assistant combine speech recognition (ASR) with NLP to understand commands and generate spoken responses. NLP handles the "understanding" part after audio is converted to text.
Healthcare
NLP extracts information from clinical notes, automates medical coding, powers symptom checkers, and helps analyze research papers at scale. It can process millions of documents in seconds.
Business Intelligence
Companies use NLP for customer sentiment monitoring, automated support tickets, email classification, contract analysis, and competitive intelligence -- turning unstructured text into structured insights.
Key Challenges in NLP
Ambiguity
"The chicken is ready to eat." Is the chicken a meal or a hungry bird? Language is inherently ambiguous, and resolving ambiguity often requires world knowledge.
Bias
NLP models learn from human-generated text, which contains societal biases. Models can perpetuate or amplify stereotypes about gender, race, culture, and other attributes.
Low-Resource Languages
Most NLP research focuses on English. Languages with less digital text -- many of the world's 7,000+ languages -- have far worse NLP performance and tool availability.
Common Sense Reasoning
Understanding that "he couldn't lift the box because it was too heavy" (vs. "he was too weak") requires common sense that remains challenging for current NLP systems.