Quick Summary
NLP enables machines to read, understand, and derive meaning from human languages using computational techniques.
Natural Language Processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and humans using natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human languages in a valuable way.
NLP Tasks
| Task | Description | Example |
|---|---|---|
| Sentiment Analysis | Determining emotion in text | Review classification |
| Named Entity Recognition | Identifying names, places, organizations | Extracting entities from news |
| Machine Translation | Translating between languages | Google Translate |
| Question Answering | Answering questions from text | Chatbots, search engines |
| Text Summarization | Creating concise summaries | News summaries |
NLP Challenges
- Ambiguity in language
- Context understanding
- Sarcasm and irony
- Multiple languages and dialects
- Domain-specific terminology
NLP Applications
- Chatbots and virtual assistants
- Email filtering and categorization
- Voice assistants (Siri, Alexa)
- Grammar checking tools
- Social media monitoring
- Document analysis
Popular NLP Libraries
- NLTK: Python NLP library
- spaCy: Industrial-strength NLP
- Hugging Face: Transformer models
- BERT: Google's language model
- GPT: OpenAI's generative models
Key Points
- AI understanding human language
- Used in chatbots and translation
- Sentiment analysis and NER
- Challenges with context
- Transformer models advanced
- Hugging Face popular platform