Advanced Learning Activities for N-Grams

1. Comparative N-Gram Analysis

Activity: Compare bigram and trigram models on the same corpus.

• Task: Build both models and analyze how sentence probability estimates differ.
• Learning Goal: Understand the impact of context window size on language modeling.
• Tools: Use the simulation or write a simple script in Python.

2. Smoothing Experiment

Activity: Explore the effect of smoothing on unseen N-Grams.

• Task: Calculate sentence probabilities with and without smoothing.
• Learning Goal: See how smoothing prevents zero probabilities and improves model robustness.
• Method: Apply Laplace smoothing to your N-Gram table.

3. Historical N-Gram Study

Activity: Investigate how N-Gram frequencies change over time.

• Task: Use the Google Books Ngram Viewer to track the popularity of phrases.
• Learning Goal: Discover trends in language usage and cultural shifts.
• Resources: https://books.google.com/ngrams

4. Build Your Own N-Gram Predictor

Activity: Implement a simple N-Gram text generator.

• Task: Write a program that generates sentences by sampling from an N-Gram model.
• Learning Goal: Apply theoretical knowledge to practical text generation.
• Tools: Python, JavaScript, or any language of your choice.

Research Topics for Advanced Study

1. N-Gram Models vs. Neural Language Models

• Research Question: How do N-Gram models compare to modern neural approaches in terms of accuracy and resource requirements?
• Applications: Low-resource language modeling, embedded systems.

2. Cross-Linguistic N-Gram Patterns

• Research Question: What N-Gram patterns are universal across languages, and which are language-specific?
• Methods: Analyze corpora from multiple languages and compare N-Gram distributions.