Natural Language Processing (NLP)
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, generate, and interact with human language, in both written and spoken form. It bridges the gap between human communication and machine processing.
Its objective is to:
- understand the meaning and intent of text;
- extract relevant information;
- generate coherent and context-aware responses;
- and enable natural human-computer interaction.
Unlike traditional rule-based language systems, modern NLP relies primarily on statistical and deep learning models trained on large text corpora to learn linguistic patterns, semantics, and context.
Comparative view
| Aspect | Traditional NLP | Modern NLP |
|---|---|---|
| Approach | Rule-based / feature based | Neural / data-driven |
| Flexibility | Low | High |
| Scalability | Limited | High |
| Language support | Language-specific development | Multilingual models |
| Adaptability | Low | High |
How does it work?
NLP models process language by transforming text into numerical representations (embeddings) that capture semantic and syntactic relationships.
These representations are then used by machine learning models to perform language-related tasks such as classification, generation, or retrieval.
Modern NLP systems are primarily based on transformer architectures, which use self-attention mechanisms to model long-range dependencies in text.