What is Natural Language Processing?
Natural Language Processing (NLP) is the branch of AI that gives computers the ability to read, understand, and generate human language. Every time you use a search engine, get an autocomplete suggestion, or ask a voice assistant a question — NLP is at work.
TIP: Scale: Humans produce roughly 2.5 quintillion bytes of text data every day. NLP is the key technology that lets machines make sense of all of it.
Core NLP Tasks
| Task | Description | Example |
|---|---|---|
| Tokenization | Split text into words/sentences | "Hello world" → ["Hello", "world"] |
| POS Tagging | Label each word's grammatical role | "Run" → Verb, "fast" → Adverb |
| NER | Identify named entities (people, places) | "Apple Inc." → ORG |
| Sentiment Analysis | Detect positive/negative/neutral tone | "Great product!" → Positive |
| Text Classification | Categorize documents into classes | Spam vs. Not Spam |
| Machine Translation | Translate between languages | "Bonjour" → "Hello" |
| Question Answering | Extract answers from context | BERT, RAG pipelines |
| Text Generation | Produce coherent text from a prompt | GPT-4, LLaMA |
The NLP Pipeline
Flow:
- Raw Text — User input or corpus
- Preprocessing — Clean, tokenize, normalize
- Features — TF-IDF, embeddings, vectors
- Model — Classifier, seq2seq, LLM
- Output — Label, translation, answer
Key NLP Libraries
| Library | Language | Best For |
|---|---|---|
| NLTK | Python | Learning NLP fundamentals, academic research |
| spaCy | Python | Production NLP: tokenization, NER, POS, fast |
| HuggingFace Transformers | Python | BERT, GPT, fine-tuning, state-of-the-art models |
| Gensim | Python | Topic modelling, Word2Vec, document similarity |
| TextBlob | Python | Simple sentiment analysis and text processing |