Unit 4: AI Applications

Lesson 2: Natural Language Processing (1 hour)

Lesson content from Unit 4: AI Applications

Lesson 2: Natural Language Processing (1 hour)

Learning Objectives

  • Understand what natural language processing is
  • Recognize how AI processes and understands text
  • Identify NLP applications
  • Use NLP tools hands-on

Materials Needed

  • Internet-connected devices
  • NLP demos and tools (translation, sentiment analysis, etc.)
  • Text examples
  • Student notebooks
  • Examples of NLP applications

Time Breakdown

  • Review computer vision (5 min)
  • Introduction to NLP (15 min)
  • How NLP works (15 min)
  • Hands-on: NLP tools (20 min)
  • Wrap-up (5 min)

Activities

1. Review Computer Vision (5 min)

  • What is computer vision?
  • How does AI process images?
  • Bridge: "Today we'll see how AI understands human language"

2. Introduction to Natural Language Processing (15 min)

What is NLP?

  • Teaching computers to understand, interpret, and generate human language
  • Like giving computers the ability to read and write
  • Processing text and speech to extract meaning

Key Tasks:

  1. Text Classification: Categorizing text (spam/not spam, sentiment)
  2. Language Translation: Translating between languages
  3. Named Entity Recognition: Finding names, places, dates in text
  4. Text Generation: Creating new text (chatbots, stories)
  5. Question Answering: Answering questions from text
  6. Summarization: Creating summaries of long texts
  7. Sentiment Analysis: Determining if text is positive/negative

Real-World Applications:

  • Search Engines: Google, Bing understanding queries
  • Translation: Google Translate, language apps
  • Chatbots: Customer service, virtual assistants
  • Email: Spam detection, auto-categorization
  • Social Media: Content moderation, sentiment analysis
  • Writing Assistants: Grammarly, autocomplete
  • Voice Assistants: Understanding spoken commands
  • Healthcare: Analyzing medical records, research papers

Why NLP Matters:

  • Most human knowledge is in text
  • Enables human-computer communication
  • Can process vast amounts of text quickly
  • Makes technology more accessible

3. How NLP Works (15 min)

The Challenge:

  • Language is complex, ambiguous, context-dependent
  • Same words can mean different things
  • Grammar rules have exceptions
  • Context matters

The Process:

  1. Tokenization: Breaking text into words/tokens
  2. Part-of-Speech Tagging: Identifying nouns, verbs, adjectives
  3. Parsing: Understanding sentence structure
  4. Semantic Analysis: Understanding meaning
  5. Context Understanding: Using surrounding text

Key Concepts:

Word Embeddings:

  • Representing words as numbers (vectors)
  • Similar words have similar representations
  • Example: "cat" and "dog" are closer than "cat" and "car"

Neural Networks for NLP:

  • RNNs: Process text sequentially (remembering previous words)
  • Transformers: Modern approach, attention mechanism
  • Large Language Models: Very large networks trained on lots of text

Example: Understanding "The bank is closed"

  • "Bank" could mean:
    • Financial institution
    • River bank
  • Context helps determine meaning
  • "The bank is closed" → Financial institution
  • "The bank of the river" → River bank

Challenges:

  • Ambiguity (words with multiple meanings)
  • Sarcasm and humor
  • Context dependency
  • Different languages, dialects
  • Slang and informal language

4. Hands-On: NLP Tools (20 min)

Activity 1: Language Translation (7 min)

  • Use Google Translate or similar
  • Translate sentences:
    • Simple: "Hello, how are you?"
    • Complex: Idioms, cultural references
    • Different languages
  • Observe: Accuracy, what works well, what doesn't
  • Try: Translate back and forth (loses meaning?)

Activity 2: Sentiment Analysis (7 min)

  • Use sentiment analysis tool
  • Analyze different texts:
    • "I love this product!" (positive)
    • "This is terrible" (negative)
    • "It's okay, I guess" (neutral)
    • "Not bad" (sarcasm? - often missed)
  • Observe: Accuracy, edge cases, limitations

Activity 3: Text Generation (6 min)

  • Use ChatGPT or similar (with guidance)
  • Try:
    • Ask questions
    • Request summaries
    • Ask it to write a short story
    • Ask about current events (note limitations)
  • Observe: Capabilities, limitations, when it makes mistakes

Reflection Questions:

  • What worked well? What didn't?
  • What surprised you?
  • How does AI understand context?
  • What are the limitations?

5. Wrap-Up (5 min)

  • NLP: AI understanding and generating language
  • Uses neural networks to process text
  • Many applications in daily life
  • Still struggles with ambiguity, context, sarcasm
  • Preview: Next lesson - Speech recognition and synthesis

Differentiation Strategies

  • Younger students: Focus on fun demos, simpler tools, hands-on exploration
  • Older students: Explore how transformers work, research specific models, analyze limitations
  • Struggling learners: Use guided exploration, simpler examples, more support
  • Advanced learners: Research specific NLP techniques, explore language models, analyze biases

Assessment

  • Participation in hands-on activities
  • Quality of observations
  • Understanding of NLP concepts
  • Reflection journal entry