N-gram Language Modelling

N-gram Language Modelling #

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Learning Objectives #

  • Explain the Markov assumption used by n-gram language models.
  • Calculate sequence probabilities using unigram, bigram, and trigram models.
  • Explain the zero-probability problem and the role of smoothing.
  • Evaluate a language model using appropriate metrics such as perplexity.

Chapter Map #

SectionTopicStatus
1N-grams
2Generalisation and Zeros
3Smoothing
4The Web and Stupid Backoff
5Evaluating Language Models

Big Picture #

1. N-grams ☆ #

Definition #

Intuition #

Key Concepts #

Formula or Model #

Worked Example #

Why It Matters in NLP #

Key Points to Remember #

2. Generalisation and Zeros ☆ #

Definition #

Intuition #

Key Concepts #

Formula or Model #

Worked Example #

Why It Matters in NLP #

Key Points to Remember #

3. Smoothing ☆ #

Definition #

Intuition #

Key Concepts #

Formula or Model #

Worked Example #

Why It Matters in NLP #

Key Points to Remember #

4. The Web and Stupid Backoff ☆ #

Definition #

Intuition #

Key Concepts #

Formula or Model #

Worked Example #

Why It Matters in NLP #

Key Points to Remember #

5. Evaluating Language Models ☆ #

Definition #

Intuition #

Key Concepts #

Formula or Model #

Worked Example #

Why It Matters in NLP #

Key Points to Remember #

Practical Exploration #

Build an n-gram language model and compare unsmoothed and smoothed predictions.

# Add a minimal, well-commented Python example here.

Comparison Table #

Concept or ModelMain IdeaStrengthLimitationTypical Use

Common Mistakes #

Practice Questions #

Key Takeaways #

Understanding Checklist #

  • I can explain N-grams without referring to notes.
  • I can explain Generalisation and Zeros without referring to notes.
  • I can explain Smoothing without referring to notes.
  • I can explain The Web and Stupid Backoff without referring to notes.
  • I can explain Evaluating Language Models without referring to notes.

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