Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. It enables machines to understand, interpret, and generate human-like text.

🔍 Key Concepts in NLP

  • Tokenization: Splitting text into individual words or phrases (e.g., "Hello, world!" → ["Hello", ",", "world", "!"])
  • Part-of-Speech Tagging: Identifying grammatical components in text (e.g., nouns, verbs)
  • Named Entity Recognition (NER): Detecting entities like people, organizations, or locations
  • Sentiment Analysis: Determining the emotional tone behind text (e.g., positive, negative)
  • Machine Translation: Converting text from one language to another (e.g., English → Spanish)
Natural_Language_Processing

🧠 Core Technologies

  • Word Embeddings: Representing words as vectors (e.g., Word2Vec, GloVe)
  • Transformer Models: Foundation for modern NLP (e.g., BERT, GPT)
  • Sequence-to-Sequence (Seq2Seq): Used in tasks like text summarization
  • Dialogue Systems: Building chatbots with intent recognition
Transformer_Model

🌍 Applications of NLP

  • Virtual Assistants (e.g., Siri, Alexa)
  • Social Media Analysis (e.g., trend detection, user sentiment)
  • Legal Document Review (e.g., automated contract analysis)
  • Healthcare NLP (e.g., patient record summarization)
Sentiment_Analysis

📘 Further Reading

For deeper insights, explore our NLP Tutorials or Language Models Documentation.

Let us know if you need help with specific NLP tasks! 🤖💬