What is NLP?
Natural Language Processing (NLP) is the field of AI focused on enabling computers to understand, interpret, and generate human language. It bridges the gap between human communication and computer understanding.
Every time you use Siri, Google Translate, or ChatGPT, you're using NLP.
Why NLP is Hard
Language is ambiguous, context-dependent, and full of exceptions. "I saw her duck" could mean watching a bird or watching someone avoid something.
Key NLP Tasks
Text Classification
Categorizing text into predefined groups. Examples: spam detection, sentiment analysis (positive/negative), topic classification.
Named Entity Recognition (NER)
Identifying and classifying names, places, organizations, dates in text. "Apple announced in Cupertino on Tuesday..." → [ORG: Apple], [LOC: Cupertino], [DATE: Tuesday].
Machine Translation
Converting text from one language to another. Google Translate, DeepL.
Question Answering
Finding answers to questions in text. Behind voice assistants and search engines.
Text Generation
Creating new text based on patterns learned from training data. ChatGPT, content writing tools.
Summarization
Condensing long text into shorter versions while preserving key information.
How NLP Works
Tokenization
Breaking text into smaller pieces (tokens). "I love AI" → ["I", "love", "AI"] or even ["I", "lov", "e", "A", "I"] for subword tokenization.
Embeddings
Converting words into numbers (vectors) that capture meaning. Similar words have similar vectors: "king" and "queen" are close; "king" and "banana" are far.
Attention
Allowing the model to focus on relevant parts of the input. In "The cat sat on the mat because it was tired," attention helps connect "it" to "cat."
Evolution of NLP
- Rule-based (1950s-1980s) — Hand-coded grammar rules
- Statistical (1990s-2000s) — Learning patterns from data
- Neural networks (2010s) — Deep learning approaches
- Transformers (2017+) — Modern LLMs like GPT, BERT
Modern NLP Capabilities
- Understanding context and nuance
- Generating coherent long-form text
- Following complex instructions
- Reasoning through multi-step problems
- Code generation and debugging
Challenges in NLP
- Ambiguity — Words and phrases with multiple meanings
- Context — Understanding references and implications
- Common sense — Knowledge that humans take for granted
- Low-resource languages — Limited training data for many languages
- Bias — Models can perpetuate stereotypes in training data
Summary
- • NLP enables computers to understand and generate human language
- • Key tasks: classification, translation, generation, summarization
- • Modern NLP uses tokenization, embeddings, and attention
- • Transformers revolutionized NLP in 2017