Main Concept
Natural Language Processing (NLP) is the field focused on enabling computers to understand, interpret, and generate human language.
It bridges the gap between how humans naturally communicate (through speech and text) and how computers process information (through structured data and code).
Core goal: Make human-computer interaction more natural by allowing machines to work with language the way humans do - understanding meaning, context, and intent rather than just matching keywords.
Common applications:
- Text analysis and sentiment detection
- Language translation
- Chatbots and virtual assistants
- Speech recognition and text-to-speech
- Document summarization
- Information extraction from text
Key Aspects
The challenge: Human language is messy, ambiguous, and context-dependent. The same words can mean different things, and we rely heavily on implied meaning, cultural references, and situational context.
Modern approach: AI/ML techniques that learn language patterns from vast amounts of text data, allowing systems to handle this complexity much better than rule-based approaches.
In essence, NLP is about teaching machines to “speak human” - both understanding what we say and communicating back in ways that feel natural to us.
NLP and AI
Traditional NLP (pre-AI era) included:
- Linguistics-based approaches - using grammatical rules, syntax trees, and linguistic theory
- Rule-based systems - hand-crafted rules for parsing, translation, and text processing
- Statistical methods - frequency analysis, n-grams, and probabilistic models
- Symbolic processing - formal logic and knowledge representation systems
- Classical information retrieval - keyword matching, TF-IDF scoring
Think of early systems like:
- ELIZA (1960s) - a simple chatbot using pattern matching and substitution rules
- Early machine translation - rule-based systems that tried to translate by applying grammatical transformations
- Spell checkers and grammar checkers - often using dictionaries and rule-based approaches
Modern AI-powered NLP revolutionized the field with:
- Machine learning models that learn language patterns from massive datasets
- Deep learning architectures like transformers (which power systems like ChatGPT)
- Contextual understanding rather than just keyword matching
- The ability to handle ambiguity and nuance much better