Core NLP Tasks and Applications

Advanced

🧩 Core Tasks in Natural Language Processing (NLP)

NLP encompasses a variety of core tasks essential for transforming raw language data into meaningful insights:

🧱 Fundamental NLP Tasks

  • ✂️ Tokenization
    Splitting text into smaller units like words or sentences.

  • 🏷️ Part-of-Speech Tagging
    Identifying grammatical categories of words.

  • 🧠 Named Entity Recognition (NER)
    Detecting entities like names, organizations, and locations.

  • 🌐 Parsing
    Analyzing sentence structure to understand syntactic relationships.

  • 😊 Sentiment Analysis
    Determining the emotional tone of a text.

  • 🔁 Machine Translation
    Converting text from one language to another.

  • 📝 Summarization
    Generating concise summaries of longer texts.

🌍 Real-World Use Cases

  • 🤖 Chatbots understanding user queries
  • 📊 Social media monitoring for brand sentiment
  • 🌎 Multilingual translation services

🚀 Key Takeaway

Mastery of these tasks forms the backbone of advanced NLP systems.

🧩 Diagram: NLP Task Map

Raw Text
   |
   v
[Tokenization]
   |
   v
[POS Tagging] --> [Parsing] --> [NER]
   |                               |
   v                               v
[Sentiment Analysis]      [Machine Translation]
             \                    /
              \                  /
               --> [Summarization]